Enhancing Evidence Translation Through Structured Clinical Reasoning: A Mixed-Methods Controlled Feasibility Study of Peer Visibility and Readiness Mechanisms in Applied Learning

ABSTRACT

In clinical environments, evidence translation is often stalled by the incomplete bridge between clinicians’ agreement with evidence and its implementation. In community oncology, where time constraints and variable resources shape care, this “know-do” gap reflects decision readiness under uncertainty. Clinicians can know the guidelines and still hesitate to act. Closing that practice gap requires more than awareness. Digital applied learning that makes peer clinical reasoning visible may support readiness mechanisms, but feasibility and mechanism signals for scalable peer visibility remain under-characterised. We evaluated feasibility and hypothesis-generating signals from clinician learners’ engagement in a digital learning experience and explored plausible mechanisms by which peer visibility may influence clinician readiness for evidence translation. We conducted a mixed-methods, controlled feasibility evaluation among NPI-verified medical oncologists (N = 104) completing a digital applied learning experience with peer visibility (n = 80) or without (n = 24). Peer-facing content was generated by clinicians as responses to questions within the education. Assessed outcomes included feasibility, engagement, and readiness for application in practice. Open-text responses were characterised descriptively. Participation was high across modules in both variants (83–96%). Median total engagement time was higher in the peer visibility variant (37.3 minutes; IQR 13.4–70.7) versus the no-peer variant (20.2 minutes; IQR 8.4–44.2). Readiness-to-apply items show a mixed/directional pattern, though ceiling effects limited the precision of between-arm comparisons. The peer visibility variant produced 1,115 structured entries versus 315 in the no-peer variant (n = 75 versus n = 22), a comparable rate per contributor in both variants. Descriptive content characterisation identified categories consistent with confidence calibration and feasibility imagining. Integrating structured peer visibility into a digital applied learning experience appears to be practical at scale and yielded hypothesis-generating signals of higher engagement and plausible readiness mechanisms. Findings support future research to isolate active components, validate readiness constructs, and test mechanism pathways supporting translation to clinical action.

Introduction

Evidence translation is increasingly challenged by a paradox of modern clinical practice: clinicians have unprecedented access to scientific information, yet timely uptake into routine care remains inconsistent, particularly in oncology, where treatment landscapes evolve rapidly. Clinicians can know the evidence and still hesitate, not from disagreement, but because real-world practice introduces uncertainty: patient complexity, workflow constraints, sequencing questions, and monitoring burden. This evidence-to-practice gap is especially consequential in community oncology settings, where delays in translating awareness into implementable action can contribute to practice variation and inconsistent care quality.

In knowledge-abundant environments, translation barriers often arise not from lack of awareness but from the practical difficulty of moving from awareness to actionability: determining applicability to specific patients, anticipating risks, and calibrating confidence under real-world constraints. Traditional education and dissemination approaches, which emphasise knowledge transfer and guideline summarisation, may be insufficient when clinicians already understand the evidence; the limiting step may instead be implementation readiness: confidence calibration, feasibility appraisal, and identification of feasible next steps.

Peer influence is a recognised driver of clinical practice patterns, but peer signals are often delivered indirectly (e.g. local opinion leaders) or through resource-intensive formats such as tumour board and journal club. Scalable digital formats incorporating “peer comparison” typically emphasise what peers chose rather than how peers reason through uncertainty, and most digital learning experiences remain light-touch, offering limited structured opportunity to rehearse reasoning or pressure-test feasibility.

The theoretical foundation for this study draws on three intersecting domains. First, social learning theory holds that durable behaviour change follows staged progression from attention to a modelled behaviour through retention, reproduction, and motivation to enact it in practice [1,2]. Second, skills- and decision-making-focused education is most effective when it includes applied practice through case studies, worked examples, feedback, and reflection [3,4], and digital delivery can extend this model to social and peer-to-peer/mentorship-based learning at scale [5]. Third, structured peer information sharing has shown direct promise for clinical judgement: Centola and colleagues found that structured information-sharing networks reduce medical errors and improve decision performance through belief revision based on peer signals [6]. Fewer studies, however, have examined whether visibility into how peers reason and operationalise evidence under real-world constraints, rather than what they simply chose, can build readiness to act within time-efficient applied learning. This study addresses that gap.

This study focuses on structured peer visibility, defined as the visibility of anonymised peer clinician perspectives emphasising clinical reasoning and implementation strategies under real-world constraints, rather than correctness comparison or social proof. We evaluated a digital applied learning pathway delivered to National Provider Identifier- (NPI)-verified medical oncologists in two variants, or study arms: applied learning with structured peer visibility versus applied learning without peer visibility. The pathway was designed to mirror common real-world engagement modes spanning education, dissemination, and scientific engagement, and the evaluation emphasised feasibility, engagement dose, readiness-to-apply intent, corpus-scale feasibility of generating peer perspectives, and plausible readiness mechanisms.

A distinguishing feature of this design is that peer-facing content was not authored in advance but produced by participating clinicians themselves and made visible to other learners, positioning the study within a tradition of continuing professional development in which learning arises through participation and shared reflection rather than transmission alone. Learners themselves likened the experience to a journal club or tumour board, though the analogy has limits: contributions were anonymised, asynchronous, and platform-mediated, so the design does not constitute a community of practice as described by Wenger-Trayner et al. [7]. What it offers instead is a scalable approximation of one element of collegial exchange, visibility into peer reasoning under uncertainty, decoupled from co-location and synchronous time. Whether this approximation confers the benefits of true collegial dialogue is an empirical question this feasibility evaluation cannot answer.

Workplace learning theory holds that professional expertise develops largely through participation in the problems of practice itself rather than detached instruction, a dynamic well documented in continuing medical education, where learning is often mediated through talk and interaction among practicing physicians [8]. Case scenario prompts approximate this mechanism, offering the ambiguity and demand for judgement of an authentic problem of practice without the exposure of a live encounter. This connects to Sargeant et al.”s [9] account of peer feedback and Lockyer et al.”s [10] R2C2 model: exposure to a colleague’s reasoning functions as social comparison that, when followed by reflection, can recalibrate a clinician’s own practice decisions, realising its full value when it translates into a concrete plan for action.

This design also engages Schön’s account of reflection-in-action rather than reflection-on-action [11]: structured prompts elicit learners’ reasoning in the course of engaging with case scenarios, and, in the peer visibility arm, expose that in-the-moment reasoning to others facing the same uncertainty, distinguishing this from post-hoc debriefing and from persuasion-based peer comparison.

Material and Methods

The primary hypothesis for this study is: A time-efficient digital applied learning experience is both feasible and scalable as an education approach for medical oncologists.

The secondary hypothesis is: Compared to an applied learning variant without peer visibility, among medical oncologists, a peer visibility variant will be associated with directional signals consistent with readiness mechanisms for evidence translation.

This mixed-methods, controlled feasibility evaluation assessed (1) feasibility and engagement dose of a time-efficient digital applied learning experience for evidence translation and (2) hypothesis-generating signals of readiness mechanisms associated with structured peer visibility, or visibility of anonymised peer clinical reasoning and implementation considerations.

Study Design

We conducted a mixed-methods, controlled feasibility evaluation comparing two experience variants delivered within the same digital applied learning pathway: (1) applied learning with structured peer visibility and (2) applied learning without peer visibility. The analytic intent was feasibility assessment and descriptive, hypothesis-generating between-arm signals.

Participants

Participants were actively practicing medical oncology physicians in the United States, drawn from community-based oncology practices. Participants were identified via a search of an NPI-verified healthcare professional database and invited by email to participate voluntarily; recruitment remained open until the target sample size was reached. The sample included 104 oncologists (peer visibility n = 80; no-peer n = 24). The peer group was over three times larger than the no-peer subsample, spanned more states (34 vs. 16), and had a more even regional distribution (Midwest, n = 21; South, n = 20; Northeast, n = 19; West, n = 17); the no-peer subsample was concentrated in the South (n = 11) and Midwest (n = 8), with minimal Northeast (n = 2) and West (n = 3) representation. Despite this geographic difference, the groups were demographically similar: nearly all participants were physicians in hospital-based or outpatient speciality settings, with identical median years of clinical experience (20 years) in both groups.

Participants were randomly assigned to the experimental intervention group (peer visibility) or control (no-peer), and each received minimal remuneration for participation. Because the evaluation focus was feasibility and mechanism plausibility, participant characteristics beyond NPI verification were not collected in a way that supports detailed subgroup analyses.

This sampling design has limitations. Voluntary email-based recruitment raises the potential for self-selection bias, as participants may differ systematically from non-participants in availability, topic interest, or willingness to engage in outside professional activities, limiting generalisability to the broader eligible clinician population. Recruitment was limited to professionals with accessible contact information in an NPI-verified database, potentially underrepresenting clinicians in less consistently captured settings, such as smaller independent practices.

Intervention

Digital Applied Learning Pathway

Both study variants consisted of a time-efficient digital applied learning pathway designed to support evidence translation through expert framing, clinical reasoning rehearsal, and practice-facing reflection. The applied learning pathway was organised within 4 separate modules: (1) faculty expert-led short videos and supporting content (e.g. peer-reviewed publications), (2) case-based interactive questions reflecting realistic patient assessment and management decisions, and (3) structured reflective prompts eliciting clinicians’ reasoning, anticipated barriers, and implementation considerations. The educational content focused on evidence translation in metastatic breast cancer, including HER2 classification, treatment initiation drivers, adverse event management, therapeutic persistence barriers, and sequencing considerations.

Study Variants (Two Arms)

Participants completed the same core learning sequence in both arms. The primary differentiator was structured peer visibility: participants in the peer visibility arm could view anonymised peer perspectives embedded within the learning flow, while participants in the no-peer arm completed the experience without access to peer perspectives. Both arms experienced the same pathway; peer reasoning visibility was the sole differentiator.

Participants were randomised to the two arms with an unequal allocation ratio of approximately 3:1, a deliberate feasibility-design decision rather than a randomisation artefact, driven by two considerations. First, the peer visibility condition required a sufficiently large pool of contributors to generate a meaningful and varied corpus of peer-facing content, a functional requirement of the intervention itself. Second, given the feasibility (rather than confirmatory) aim of this study, resources were concentrated on characterising engagement and mechanism signals within the intervention arm, with the no-peer arm serving as a smaller directional comparator rather than a powered control.

Peer Visibility Operationalisation

Peer visibility was operationalised as structured visibility of anonymised peer perspectives, framed as clinical reasoning and implementation strategies under real-world constraints (e.g. feasibility, monitoring, sequencing logic) rather than correctness scoring or normative “most peers chose X” comparison, intentionally preserving clinician autonomy while making peer reasoning visible as a potential readiness mechanism.

Outcomes and Measures

Feasibility and Participation

Feasibility was assessed via arm-assigned participation counts and module-level participation patterns, defined as presence of at least one response (i.e. a non-missing response) within a module.

Engagement Dose as Time-On-Task

Learning experience platform-recorded time-on-task data were summarised descriptively for total engagement time in the experience pathway (EP total time, where EP refers to the full learning experience pathway) and total video content viewing time. Because time-on-task measures may reflect pauses or interruptions, medians and interquartile ranges (IQRs) are reported for interpretability.

Readiness-To-Apply (Intent)

Readiness-to-apply intent was assessed using Likert-type items (“How likely are you to apply …”) summarised descriptively by module as Top-2-box (Somewhat likely/Very likely) and “Very likely”. Analytic N varies by module due to item-level nonresponse; comparisons are interpreted as directional given feasibility intent and anticipated ceiling effects. Ceiling effects in broad intention items are treated as a methodological signal, suggesting that implementation specificity in open-text responses may serve as a more sensitive endpoint in motivated clinician samples.

Open-Text Feasibility as Corpus Scale and Unique Contributors

Structured written perspectives were captured from participants in both study variants. Corpus scale (total entries) and unique contributors were summarised as feasibility signals of perspective generation at scale.

Both arms received identical reflective prompts and produced written responses under the same elicitation conditions; visibility was the sole differentiating component. In the peer visibility arm, these responses entered the pool displayed to subsequent participants, whereas in the no-peer arm the same writing was captured but not displayed.

Content Characterisation (Peer Visibility Condition)

Free-text responses were reviewed to produce a descriptive content characterisation of the corpus, rather than a formal qualitative analysis. One researcher (CP) read responses across modules and grouped recurring content into descriptive categories reflecting the kinds of reasoning and implementation considerations learners described; categories were developed inductively during review rather than applied from a pre-specified codebook, were not quantified, and were not compared between arms (Table 6). This distinction matters methodologically: content characterisation aims to illustrate the range and texture of what participants wrote, whereas qualitative analysis aims to produce validated, generalisable themes and conventionally requires multiple coders and reliability assessment. Because this study pursued the former, a second coder and inter-rater reliability assessment were not applied at this stage. A three-point scheme for categorising the specificity of intended actions (level of detail, timeframe, and action specificity) was developed for use in subsequent confirmatory work and was not applied here.

Table 6.

Open-text mechanism signals among clinicians with peer perspective visibility.

Mechanism signal	What it suggests (plausible pathway)	Example phrasing observed in responses*
Confidence calibration	Peer perspectives support reassurance, normalisation of uncertainty, or fine-tuning without implying a normative “right answer”	“bolstered confidence”; “reinforced my approach”
Feasibility imagining and implementation planning	Peer perspectives surface operational considerations (workflow, monitoring, sequencing, team roles, constraints) relevant to applying evidence in practice	“monitoring plan”; “workflow”; “time is the constraint”
Incremental refinement vs. wholesale change	Mechanism may operate through small directional adjustments rather than reversals	“shift a couple of degrees”; “not 180 degrees”
Credibility cues (peer attribution)	Peer-attributed cues may support attention to, or consideration of, implementation-relevant perspectives	“validated oncologists”; “more credible”
Familiar professional norm framing	Experience is interpreted through familiar peer-learning contexts, supporting engagement and reflection	“journal club”; “tumor board”

*Example phrasing is presented as brief, de-identified micro-excerpts to illustrate the type of language observed and should not be interpreted as comprehensive qualitative evidence or as frequency estimates.

The open-text corpus differs from interview or focus-group data in ways that affect how adequacy, reflexivity, and trustworthiness should be assessed. Adequacy concerns coverage rather than sampling: every participant who completed a module contributed responses (75/80 in the peer visibility arm; 22/24 in the no-peer arm), so the relevant question is cohort coverage rather than a saturation point. Against the dimensions of information power [12], the study aim is narrow, prompts elicited dense and specific responses, established theory informs interpretation, and the analysis is cross-case rather than in-depth single-case. Responses are participants’ own written words, captured verbatim without transcription, interviewer probing, or rapport effects. Reflexivity is relevant because the study team authored the prompts and designed the learning experience under evaluation, shaping both what was asked and what the authors were disposed to notice. Excerpts are presented as illustrations of corpus content rather than analytic findings, and no between-arm qualitative comparison is claimed; that comparison is identified as a priority for subsequent work. Responses within the peer visibility arm are also non-independent, since later entries were composed in the presence of the growing corpus, a property of the mechanism under study rather than an artefact to remove, but one that constrains inference. Reflexive thematic analysis [13] was considered but not adopted, as the corpus consists of short, prompt-constrained entries rather than extended accounts suited to interpretive theme generation.

Brief de-identified micro-excerpts were selected to illustrate the types of reasoning and implementation considerations observed (Table 6). Feature-level interaction telemetry (e.g. depth of exposure to peer perspective content or within-module navigation patterns) was not analysed in this feasibility evaluation. Our mechanism interpretation therefore draws on arm assignment, overall engagement dose, and descriptive content-characterisation signals among clinicians exposed to peer visibility; all between-arm inferences are treated as hypothesis-generating.

Analysis

Quantitative outcomes are reported descriptively by arm as feasibility and hypothesis-generating signals. Engagement dose and readiness-to-apply intent outcomes are summarised using descriptive statistics; medians and IQRs are emphasised for time-on-task interpretability. Open-text outputs are summarised as corpus-scale feasibility signals, and content-characterisation findings are presented to illuminate plausible readiness mechanisms. Between-arm contrasts are interpreted as directional and hypothesis-generating due to feasibility scope, arm imbalance, and ceiling effects in broad intention measures.

Results

Feasibility and Participation (Primary Hypothesis)

Module Participation and Feasibility

A total of 104 NPI-verified oncologists entered the learning platform and were assigned to the peer visibility (n = 80) or no-peer condition (n = 24). Across the four modules in the learning experience, participation (defined as ≥1 non-missing response within the module) was high in both arms, supporting feasibility of the applied learning pathway at scale (Table 1). Participation ranged from 85.0% to 93.8% across modules in the peer visibility arm and 83.3% to 95.8% in the no-peer arm. Analytic n values vary by module due to item-level nonresponse and are reported per module (Table 2; Table 3).

Table 1.

Arm assignment and module participation (feasibility denominators) participation defined as ≥1 non-missing response within the module.

Metric	Peers (arm n = 80)	No-peers (arm n = 24)
Arm-assigned participants (entered educational experience via digital platform)	80	24
Module 1 participation, n (%)	75 (93.8%)	23 (95.8%)
Module 2 participation, n (%)	73 (91.3%)	21 (87.5%)
Module 3 participation, n (%)	68 (85.0%)	20 (83.3%)
Module 4 participation, n (%)	68 (85.0%)	20 (83.3%)

Table 2.

Analytic sample sizes for reported endpoints.

Endpoint	Peers visibility analytic n	No-peers analytic n	Notes on denominator / linkage
Engagement dose, time-on-task (EP total time)	46	22	Analyses restricted to participants with non-missing time fields and known arm label (summarised in Table 3).
Video content total time (seconds)	45	20	Subset with non-missing video timing fields and known arm label (summarised in Table 3); Ns differ from EP total time due to field-level missingness.
Readiness-to-apply (Likert-type), Module 1	75	21	Analytic n reflects non-missing readiness responses for that module (summarised in Table 4).
Readiness-to-apply (Likert-type), Module 2	73	20	Same as above.
Readiness-to-apply (Likert-type), Module 3	61	20	Same as above.
Readiness-to-apply (Likert-type), Module 4	66	20	Same as above.
Open-text corpus volume (rows)	1,115	315	Counts reflect compiled open-text datasets by arm (summarised in Table 5).
Unique open-text contributors (NPI)	75	22	Unique contributor counts by arm (summarised in Table 5).

Table 3.

Engagement dose (time-on-task) distributions in the timing analytic subset primary summary uses median (IQR) for interpretability.

Metric	Group	n	Median (P25–P75)	Mean	Min	Max
EP total time (minutes)	No-peers	22	20.15 (8.37–44.17)	41.19	0.22	284.10
EP total time (minutes)	Peers	46	37.30 (13.37–70.74)	73.67	0.90	498.10
Video content total time (seconds)	No-peers	20	96.85 (47.32–277.24)	148.82	0.61	277.27
Video content total time (seconds)	Peers	45	277.24 (277.24–277.24)	221.10	7.12	277.40

Time-on-task values reflect platform-recorded timing fields for participants with non-missing timing data and a linkable arm label (Table 2). Video content total time is bounded by video length; values near 277 seconds indicate viewing to the end of the video. EP = Experience Pathway (the full applied learning pathway).

Engagement Dose and Readiness Mechanism Signals (Secondary Hypothesis)

Engagement Dose

Time-on-task distributions were right-skewed; therefore, medians and interquartile ranges (IQRs) are reported for interpretability. Engagement dose analyses were restricted to participants with non-missing timing fields and a linkable arm label and are summarised in Table 3. Analytic completion for timing measures differed by arm, which should be considered when interpreting the between-arm engagement dose comparison. Median EP total time was higher in the peer visibility condition than the no-peer condition, with wide variability in both arms, consistent with the hypothesis that peer reasoning visibility increases engagement with the same content. Values near 277 seconds for video content indicate viewing to the end of the video, which explains the concentrated distribution observed in the peer visibility arm.

Readiness-To-Apply (Intent) and Ceiling Effects

Readiness-to-apply results were mixed across the four assessed items: three of four favoured the peer visibility arm (HER2-low publication, +9.1 points; sequencing case, +13.9 points; AE management publication, +12.7 points), while one reversed by nearly 19 points (AE management case: 71.2% vs. 90.0%). We report all four items evenhandedly and attribute the reversal to instability at small control-arm ns (20–21), where a single respondent shifts a reported proportion by roughly five percentage points, relevant context for the between-arm differences reported in Results (Table 4), rather than to a true between-arm difference.

Table 4.

Readiness-to-apply intent by module (top-2-box and “Very likely”).

Outcome item (module)	Group	N	Top-2-box %	“Very likely” %
Apply insight from HER2-low publication	No peers	21	90.5	76.2
	Peers	75	96.0	85.3
Apply insight from AE management case	No peers	20	100.0	90.0
	Peers	73	93.2	71.2
Apply insight from sequencing case	No peers	20	85.0	55.0
	Peers	61	95.1	68.9
Apply insight from AE management publication	No peers	20	95.0	60.0
	Peers	66	95.5	72.7

Top-2-box = “Very likely” + “Somewhat likely”. N reflects participants with non-missing responses for that module (item-level nonresponse leads to module-specific analytic Ns).

Directionally, community oncologists in the peer intervention group reported being more likely to apply and share learnings in clinical practice. Specific areas of directional clinical application included biomarker testing, treatment sequencing, and adverse event management. Directional, nonsignificant differences were consistent across measures of evidence-aligned decision-making (e.g. in case-based scenarios). Directional differences may be most detectable as shifts in “Very likely” endorsement or as differences in implementation specificity and actionability in open-text responses, rather than large separations in Top-2-box (Table 4).

Corpus-Scale Feasibility of Peer-Generated Perspectives

Structured written perspectives were captured in both variants; in the peer visibility condition, participants could view anonymised peer perspectives within the applied learning experience. Structured perspectives were generated at a comparable rate per contributor in both conditions, supporting the feasibility of eliciting and surfacing peer clinical reasoning at scale as a byproduct of applied engagement (Table 5). The peer visibility variant produced 1,115 structured entries versus 315 in the no-peer variant (n = 75 versus n = 22), a comparable rate per contributor in both variants (14.9 versus 14.3 entries).

Table 5.

Open-text corpus scale by arm (feasibility of generating real-world implementation perspectives).

Group	Unique clinicians (NPI)	Total open-text entries (rows)
Peers	75	1,115
No peers	22	315

Counts reflect all captured open-text entries within each arm, compiled from module prompts and reflections. In the peer visibility condition, participants could view anonymised peer perspectives within the experience; in the no-peer condition, participants responded without visibility into peer perspectives. Results are reported descriptively and are not intended as direct per-prompt comparators.

Mechanism Signal Characterisation (Peer Visibility Condition)

Open-text responses in the peer visibility arm were reviewed descriptively to characterise hypothesis-generating mechanism signals relevant to readiness. Clinicians described peer visibility as confidence-bolstering and validation-supporting, and they referenced incremental shifts rather than wholesale reversals. Responses commonly reflected confidence calibration (validation/normalisation of uncertainty), feasibility imagining (workflow constraints and solutions, sequencing, monitoring, team-based considerations), and incremental refinement of intended practice approaches. Illustrative response language regarding visibility into peer clinician responses and decision making included phrases such as: “It reinforces my clinical impressions by showing how peers approach the problem”; “It helps bolster my confidence in choosing a clinical pathway”; “It could tip my clinical decision-making in a real case”; and “All of this improves your clinical judgment and how you approach patients”. These observations are presented as mechanism plausibility signals and should inform future confirmatory work using pre-specified qualitative methods and validated readiness constructs (Table 6).

Discussion

Principal Findings

This mixed-methods controlled feasibility evaluation suggests that a time-efficient applied learning pathway integrating structured peer visibility can be implemented at scale among NPI-verified oncologists and provides hypothesis-generating signals related to readiness mechanisms associated with peer visibility. The peer visibility condition was associated with a higher median engagement dose (time-on-task), consistent with the possibility that visibility into peer reasoning is experienced as more relevant or credible and supports deeper reflective processing, while output per contributor was comparable across arms. Because this evaluation did not analyse feature-level telemetry, mechanistic interpretation rests on arm assignment, overall engagement, and qualitative findings rather than exposure patterns. Because most oncology care occurs in community settings, these feasibility and mechanism signals are especially relevant to scalable evidence translation approaches that fit real-world workflow constraints.

Mechanism Interpretation: From Agreement to Implementation-Specific Readiness

Clinician readiness-to-apply intent items were high across modules (Top-2-box generally 85–100%), consistent with ceiling effects; directional differences varied by module, supporting the need to interpret Likert-type intention cautiously and prioritise mechanism-relevant outcomes such as implementation specificity and feasibility planning. We propose this pattern provides a methodological insight rather than only a limitation: in motivated clinician samples, open-text implementation specificity may be a more sensitive endpoint, potentially capable of detecting shifts in decision-readiness that broad intention assessment items cannot. Accordingly, mechanism plausibility is interpreted using implementation-specific open-text indicators (e.g. feasibility planning, barrier articulation, and confidence calibration), rather than relying on global intention measures alone (Table 4; Table 6). This content characterisation among peer-exposed clinicians supports a plausible mechanism in which peer visibility functions primarily through confidence calibration/validation and feasibility/implementation imagining rather than persuasion or forced conformity. The micro-excerpts in Table 6 are brief, de-identified fragments (a few words each) included to illustrate the types of language observed in open-text responses among clinicians exposed to peer visibility. They are provided as supportive context for mechanism plausibility and are not intended as comprehensive qualitative evidence, frequency estimates, or a reproducible qualitative sampling frame.

Reliance on self-reported readiness, rather than objective measures of practice change, is an important limitation. The intention–action gap has been described in literature, such as by Saddawi-Konefka et al [14], specifically concerning implementation intentions, such as concrete, if-then plans, that are more predictive of subsequent behaviour than general commitment to change. Our structured reflective prompts elicited this specific form of intention (concrete next steps, anticipated barriers, and implementation considerations), which we highlight as a rationale for prioritised behavioural or structured follow-up study in future confirmatory work.

An alternative explanation should be considered: learners in the peer visibility arm knew their anonymised responses would be shared, so differences in response character may reflect an audience effect, prompting more considered articulation, rather than the confidence calibration and feasibility appraisal mechanisms proposed here. The present design cannot distinguish these explanations; doing so would require an arm in which responses are elicited but not shared, which was not included here.

Implications for Evidence Translation Stakeholders

The peer-facing content in this study was not curated or scripted, but the authentic clinical reasoning of participating clinicians, generated during their own learning and shared with others. We propose this is the study’s clearest point of alignment with the Special Collection’s theme of human connection: what learners saw was not a persuasion device, but a window into how a peer actually reasoned through the same clinical uncertainty they faced, which is the closest analogue to professional dialogue that an anonymised, scalable format can support.

These signals suggest that implementation readiness is a measurable, designable target, and that peer reasoning visibility is a candidate mechanism worth further testing. This evaluation does not claim downstream behaviour change; confirmatory work is needed to isolate active components and test dose–response and mechanism pathways (see Future Research).

Conclusion

Integrating structured peer visibility into a time-efficient digital applied learning experience was practical at scale and yielded hypothesis-generating signals consistent with readiness mechanisms for evidence translation. Findings extend prior structured peer information-sharing evidence, most notably Centola et al [6], by shifting the peer signal from “what peers chose” to visibility into how peers reason under real-world constraints, generating the hypothesis that reasoning visibility, rather than social conformity, is the active ingredient. As aligned with the Special Issue theme, digital innovation here appears to support, rather than replace, the human dimensions of collaboration and shared learning.

Limitations

This feasibility evaluation was not designed to establish definitive causal effects on downstream clinical behaviour or patient outcomes. Readiness measures were self-reported and showed ceiling effects. Feature-level telemetry was not analysed. Content characterisation was performed only for the peer visibility arm, so no between-group qualitative comparison was undertaken.

Learners volunteered to enrol and received remuneration for participation, which may have produced a sample more motivated and more receptive to digital CME than the broader community oncology population; this, together with the modest and imbalanced sample size (n = 80 vs. n = 24) and the ceiling effects observed in readiness-to-apply intent items, limits the strength of the conclusions that can be drawn and should be considered when interpreting the reported findings.

Analytic completion for timing measures differed by arm (total experience time available for 57.5% of the peer visibility arm versus 91.7% of the no-peer arm; video timing for 56.2% versus 83.3%), and this differential missingness could bias the engagement-dose comparison in either direction.

This content characterisation was performed descriptively by a single researcher, without a pre-specified coding frame, a second coder, or an assessment of inter-rater reliability. This single-coder approach was appropriate to the study’s directional, hypothesis-generating aim, which was focused on illustrating the range and character of corpus content rather than on producing rigorously validated qualitative themes; it was not intended to meet the standards of a formal thematic analysis. Future confirmatory work aimed at establishing qualitative findings should incorporate multiple coders, a pre-specified coding frame, and reliability assessment.

Additional limitations include the limited participant characterisation beyond NPI verification; the focus on a single clinical domain (oncology) within a U.S. physician sample, which may limit generalisability to other specialities, care settings, or international contexts; and the restriction to community-based oncologists receiving remuneration for participation, which may not reflect the full range of clinical practice environments or engagement patterns.

Significance

Despite the centrality of clinicians as end users, CME programmes are frequently designed and evaluated without meaningful clinician input, undermining relevance and effectiveness [15]. This study integrated clinician input and feedback throughout the design and evaluation of the digital applied learning experience with structured peer visibility.

As advances in breast cancer research continue to refine disease classification and expand therapeutic options, applying new evidence in clinical practice has become increasingly complex; the challenge in day-to-day oncology care is often not awareness of emerging evidence, but determining how it should be interpreted and applied across diverse patient scenarios and treatment sequencing decisions.

This challenge is especially acute for busy community oncologists, where time to absorb new literature is limited even as expectations for evidence-based precision care continue to grow. The digital applied learning approach examined here addresses a question of real clinical importance: whether making expert guidance and peer clinical reasoning visible can support clinician readiness to apply evidence in practice.

Future Research

This feasibility evaluation is best interpreted as a first step in a broader research programme on applied learning mechanisms for evidence translation. Future confirmatory work should employ balanced (e.g. 1:1) allocation informed by an a priori power calculation, with pre-registration of a minimum detectable effect size and consideration of block randomisation or stratification by practice site, to move between-arm contrasts from directional to confirmatory.

Next-phase studies should address the following specific priorities:

• Active component isolation. Disentangle the contributions of expert framing, applied case reasoning, and peer visibility via factorial or sequential designs to clarify which components drive readiness signals.

• Dose–response and mediation pathways. Leverage feature-level telemetry to test dose–response relationships between peer exposure and readiness mechanisms, including an arm in which responses are elicited but not displayed, to isolate the visibility mechanism and test the audience-effect explanation described above.

• Segmentation and mechanism targeting. Identify which clinicians benefit most from peer visibility (e.g. lower baseline confidence, fewer peer-consultation resources) and where confidence-calibration shifts are most detectable.

• Readiness construct validation. Pre-specify and validate readiness constructs beyond broad Likert-type intention items, including implementation specificity, calibrated confidence, and context-sensitive outcomes (e.g. acceptability, feasibility, appropriateness).

• Longitudinal follow-up. Extend follow-up to assess persistence of learning, including behavioural or structured self-report endpoints at a defined interval, to test whether readiness signals translate into sustained practice change

• Replication and generalisability. Replicate findings across additional clinical topics and audiences, evaluate performance with repeated exposures over time, and test generalisability to dissemination-oriented interventions reflecting real-world workflows.

Disclosure Statement

No potential conflict of interest was reported by the author(s).