Methodological reporting in feasibility studies: a descriptive review of the nursing intervention research literature

Abstract

Background

In reaction to weaknesses in feasibility studies reporting, the Consolidated Standards of Reporting Trials (CONSORT) statement published an extension for feasibility studies in 2016.

Aim

The aim of this study was to systematically review and appraise the reporting of feasibility studies in the nursing intervention research literature based on the CONSORT statement extension for feasibility studies.

Method

Papers published prior to January 2018 that described feasibility studies of nursing interventions were retrieved. Components of feasibility studies were coded, and code frequencies were analysed.

Results

The review included 186 papers. Although most papers (n = 142, 76.3%) included the label ‘pilot’ or ‘feasibility’ in their title, reporting for other components generally did not adhere to one or several CONSORT recommendations. Most papers reported objectives (n = 116, 62.4%), designs (n = 95, 51%), or rationales for sample size (n = 165, 88.7%) that were incongruent with the purpose of feasibility studies.

Discussion

This review results in two main implications for nursing research. First, we noted that the reporting of feasibility studies is weak. While all papers described feasibility studies, almost half focused exclusively on testing the effectiveness of an intervention. Second, we identified rationales for sample size along with key references that could offer guidance in reporting feasibility studies while being coherent with the CONSORT recommendations.

Background

The number of feasibility studies being published has grown in the last decade and researchers now recognise their importance in the design and evaluation of complex interventions (Craig et al., 2008; Day et al., 2015). In the literature ‘feasibility studies’ is an umbrella term that encompasses randomised pilot studies, non-randomised pilot studies and other non-pilot feasibility studies (Eldridge et al., 2016a, 2016b; Feeley and Cossette, 2015a, 2015b) (see Table 1). The purpose of a feasibility study is not to test the effectiveness of an intervention, but rather to prepare a full-scale trial by investigating features of a research protocol that could hamper its success, such as uncertainties about recruitment procedures or data collection methods (Eldridge et al., 2016a, 2016b). Accordingly, feasibility studies are highly valued by funding agencies in their decision to support larger trials.

Table 1.

Distinction of the subtypes of feasibility studies.

Type of study	Definitions from Defining Feasibility and Pilot Studies in Preparation for Randomized Controlled Trials: Development of a Conceptual Framework (Eldridge et al., 2016b)
Feasibility studies
Randomised pilot studies	‘Studies in which the future RCT, or parts of it, including the randomization of participants, is conducted on a smaller scale (piloted) to see if it can be done. Thus, randomized pilot studies can include studies that for the most part reflect the design of a future definitive trial but, if necessary due to remaining uncertainty, may involve trying out alternative strategies, for example, collecting an outcome variable via telephone for some participants and online for others.’
Non-randomised pilot studies	‘Studies in which all or part of the intervention to be evaluated and other processes to be undertaken in a future trial is/are carried out (piloted) but without randomization of participants.’
Other non-pilot feasibility studies	‘Studies that are not pilot studies are those in which investigators attempt to answer a question about whether some element of the future trial can be done but do not implement the intervention to be evaluated or other processes to be undertaken in a future trial, though they may be addressing intervention development in some way.’

An ongoing critique of feasibility studies is that they report objectives similar to those of full-scale trials without sufficient statistical power to achieve those aims (Arain et al., 2010; Kistin and Silverstein, 2015). This is problematic, as the results from underpowered studies risk being misinterpreted (over- or underestimation or effect sizes) and lead to biased recommendations regarding the value of an intervention (Arain et al., 2010; Kistin and Silverstein, 2015).

The inconsistent use of the term ‘feasibility’ is deemed to reflect a lack of agreement and guidance on the conduct and reporting of such studies (Eldridge et al., 2016b). In recent years, the scientific community has come together in hopes of addressing this issue and created guidelines to support the design and reporting of feasibility studies. In 2015, nursing researchers Feeley and Cossette (2015a, 2015b) published guidance on the purpose and conduct of feasibility studies and highlighted their role in assessing not only the feasibility, but also the acceptability of interventions. One year later, the Consolidated Standards of Reporting Trials (CONSORT) statement – the reference for reporting randomised trials – added an extension for feasibility studies in reaction to severe weaknesses in reporting (Eldridge et al., 2016a). In comparison with the original CONSORT statement, the extension added the following recommendations for reporting key components that are characteristic of feasibility studies:

• In their title, feasibility studies must be clearly identified as such.

• The background of a feasibility study must include the rationale for the larger trial and the reasons for conducting a feasibility study first, which should be coherent with objectives of such studies.

• Objectives that are appropriate for feasibility studies include investigating any component of a research protocol or intervention that is uncertain and could hamper the success of a larger trial (Eldridge et al., 2016a, 2016b; Feeley and Cossette, 2015a, 2015b).

• The study design must be labelled with ‘pilot’ or ‘feasibility’ (e.g. pilot randomised controlled trial).

• For sample sizes, authors should provide a rationale for the number of participants recruited but are not expected to report the calculations by which the numbers were determined (e.g. power calculations).

With respect to the latter recommendation, determining the sample size for a feasibility study is a topic of debate. It is widely accepted that power calculations for testing the effectiveness of an intervention (formal hypothesis testing) are not appropriate to determine the sample size for a feasibility study as they are not congruent with the purpose of such study (Eldridge et al., 2016a). To be congruent with the purpose of feasibility studies, a more acceptable approach is to determine key objectives to be achieved (e.g. recruitment and retention rates), and to adjust numbers to ensure a desired degree of precision around these estimates (Eldridge et al., 2016a). Another approach is to use a proportion (e.g. 10–15%) of the expected sample size of the full-scale trial (Cocks and Torgerson, 2013; Eldridge et al., 2016a; Whitehead et al., 2016).

The extent to which these recommendations have influenced the use and reporting of feasibility studies in nursing intervention research remains unclear. Thus, our objective was threefold: (a) to systematically review the literature on feasibility studies in nursing intervention research; (b) to assess the reporting of characteristic components of feasibility studies in nursing intervention research, based on the CONSORT statement extension for feasibility studies; (c) to identify the rationales and key references used by authors to support the sample sizes in their feasibility studies that are coherent with the CONSORT recommendations.

Materials and methods

This descriptive review (Paré et al., 2015) was conducted to examine literature and assess the reporting of feasibility studies in nursing intervention research. Through structured search methods, descriptive reviews aim to identify interpretable patterns and gaps in the literature with respect to pre-existing propositions, theories, methodologies or findings (Paré et al., 2015). In the descriptive review reported here, following the formulation of our aims we performed five steps:

1. developed the search strategy based on key feasibility study literature (for example: Eldridge et al., 2016a; 2016b; Feeley and Cossette 2015a, 2015b);

2. conducted a systematic search of multiple databases to identify feasibility studies in nursing intervention research;

3. selected studies using pre-established eligibility criteria;

4. extracted the data focused on key features of feasibility studies;

5. synthesised and analysed data.

Steps 1 and 2: search strategy and systematic search

A search strategy was defined by a librarian in collaboration with review authors. The search strategy used a combination of keywords and medical subheadings related to feasibility studies and nursing interventions (e.g. ‘Pilot Projects’, ‘Feasibility Studies’, ‘Nursing’, ‘Intervention’).

We searched seven electronic bibliographical databases in January 2018 for eligible primary research articles: Cumulative Index to Nursing and Allied Health Literature (CINAHL), via EBSCOhost; Embase, via Ovid SP; Google Scholar; PsycINFO, via APA PsycNet; PubMed (including MEDLINE), via NCBI; Web of Science—Science Citation Index (SCI) Expanded and Social Sciences Citation Index (SSCI), via Clarivate Analytics.

Step 3: eligibility criteria and selection of papers

To be included papers had to describe a feasibility study of a nursing intervention delivered to patients. Nursing intervention was defined as ‘treatments, therapies, procedures, or actions implemented by healthcare professionals to and with clients, in a particular situation, to move the client’s condition toward desired health outcomes that are beneficial to the clients’ (Sidani and Braden, 2011, p. 17). In terms of study design, no restrictions were used aside from our search strategy keywords which already included ‘Pilot Projects’ and ‘Feasibility Studies’. Papers written in English and French from 2015 to 2017 were retained to portray the reporting practices around and after the publication of recommendations cited in this paper’s introduction (Eldridge et al., 2016a, 2016b; Feeley and Cossette 2015a, 2015b).

Exclusion criteria were: (a) secondary analysis, literature review, or meta-analysis; (b) conference abstract; (c) full-scale randomised controlled trial; (d) intervention delivered to healthcare professionals; and (e) intervention not led or delivered by a nurse.

All citations were imported in EndNote X8.1 and duplicates were removed. Based on titles and abstracts, two independent researchers (TM, MHG) screened the first 100 citations. Given the high inter-rater agreement – Kappa reached 0.86 (95% CI, 0.84–0.88) – the remaining citations (n = 909) were split into two equal sets and each was screened by one researcher (TM or MHG). When a researcher doubted whether to include a citation or not, another researcher (PL) examined the citation and consensus was reached by discussion. Following this, full texts were retrieved for the papers selected, and additional papers were excluded per criteria described above.

Step 4: data extraction

Five researchers (TM, MHG, PL, MAMC, GF) extracted the following data for included studies: year of publication, journal, country, specialty, title, objectives, study design and rationale for sample size. Double data extraction was performed for 12% of the sample as suggested for medical record review (Worster and Haines, 2004). As the extractor agreement was high, the rest of the data was extracted by only one extractor for the remainder of the sample.

Step 5: data synthesis and analysis

Studies were first codified to extract characteristics of interest; each study was treated as a unit of analysis. Then, a frequency analysis was conducted to identify patterns in order ‘to represent the state of the art in a research domain’ (Paré et al. 2015, p. 186). Descriptive statistics (frequencies) were used to report the year of publication, journal, country and specialty. For titles, objectives and designs, papers were coded based on labels used by authors: ‘pilot’, ‘acceptability’, ‘feasibility’, or ‘effectiveness’ (including ‘efficacy’, ‘effect’, or ‘impact’). Additional wording used to characterise a study’s aim (e.g. ‘refining’ or ‘developing’ an intervention, ‘preliminary’ assessment of the intervention) or design (e.g. ‘randomised’, ‘qualitative’) were also coded. These labels were selected based on the CONSORT guidelines and the writings of key authors in the pilot literature (Arain et al., 2010; Cocks and Torgerson, 2013; Eldridge et al., 2016a, 2016b; Feeley and Cossette, 2015a, 2015b; Hertzog, 2008; Lancaster et al., 2004; Leon et al., 2011; Thabane et al., 2010). Based on this literature, we identified labels that reflect adequate elements of feasibility studies and others that reflect a misconception (Table 2). Descriptive statistics were also stratified by year of publication to identify any trends relative to publications before or after the publication of the CONSORT recommendations.

Table 2.

Codification scheme for data analysis.

Characteristics of feasibility studies	Labels representing adequate understanding – congruent with reporting standards	Labels reflecting misconception of elements – incongruent or with reporting standards or incomplete
Title	‘pilot’ ‘acceptability’ ‘feasibility’ ‘preliminary effectiveness’ ‘preliminary efficacy’ ‘preliminary effect’ ‘preliminary impact’	‘effectiveness’ ‘efficacy’ ‘effect’ ‘impact’ *without any wording associated with preliminary assessments.
Objectives	‘assessment of acceptability’ ‘assessment of feasibility’ ‘refining an intervention’ ‘developing an intervention’ ‘preliminary assessment of the intervention’	‘assessing effectiveness’ ‘assessing efficacy’ ‘assessing effect’ ‘assessing impact’ *any wording associated with hypothesis testing relative to the intervention’s effect.
Study design	‘pilot’ ‘acceptability’ ‘feasibility’ AND ‘randomised’ ‘non-randomised’ ‘quasi-experimental’ ‘qualitative’ ‘mixed’ ‘pre-post’ ‘observational’ *any wording describing the study design.	No use of the word pilot or feasibility or acceptability. Study design not identified in the paper.
Rationale for the sample sizes	Labels were determined based on papers (see analysis section).
	As per the CONSORT recommendations, a rationale was expected for the number of participants recruited but we were not expecting calculations by which the numbers were determined (e.g. power calculations).	Rationale relative to hypothesis testing or no rationale provided at all to explain the choice of sample size.

As the guidance for the appropriate rationales to support the sample size in feasibility studies is still vague, we coded the rationales for sample sizes from the papers using an inductive coding procedure, that is creating and adjusting codes depending on what was described in the papers. The analysis was mainly descriptive and based on the frequencies of codes for each category of interest.

Results

The flowchart of the literature is presented in Figure 1. Titles and abstracts were screened for 909 papers. Following this, full texts were retrieved for the papers selected (n = 206), and additional papers were excluded per criteria described above (n = 20). Leaving 186 papers to be included in the analyses.

Figure 1.

Flow diagram of the article selection process.

Papers included in the review were published in 2015 (n = 83, 44.6%), 2016 (n = 67, 36.0%), and 2017 (n = 36, 19.4%); see Supplemental Digital Content 1 for the list of papers reviewed. Papers were published in 116 different journals (Supplemental Digital Content 1). Papers originated from North America (n = 105, 56.5%), Asia (n = 36, 19.4%), Europe (n = 26, 14.0%), Oceania (n = 13, 7.0%) and South America (n = 4, 2.2%). The most frequent specialties were geriatrics (n = 32, 17.2%), paediatrics (n = 24, 12.9%), oncology (n = 28, 15.1%) and chronic care (n = 25, 13.4%).

The majority of papers included the label ‘pilot’ or ‘feasibility’ in their title (n = 142, 76.3%); the remainder included ‘pilot’ or ‘feasibility’ in their abstract (n = 32, 17.2%) or main text (n = 12, 6.5%). In addition to the label ‘pilot’ or ‘feasibility,’ the title of 26 papers (14%) included labels suggesting formal hypothesis testing (e.g. ‘efficacy’, ‘effectiveness’, ‘effect’ or ‘impact’).

As presented in Table 3, the objectives reported in 69 papers (37.1%) were solely to refine an intervention/protocol, or to test its acceptability or feasibility. In 32 papers (17.2%), objectives related to the feasibility and acceptability of an intervention/protocol were combined with objectives to test the effectiveness of an intervention. One paper’s objective was to calculate the sample size for a larger trial. In the rest of the papers (n = 84, 45.2%), objectives were solely to investigate the ‘efficacy’, ‘effectiveness’, ‘effect’, or ‘impact’ of an intervention.

Table 3.

Descriptive statistics for characteristics of feasibility studies.

	Papers (n total = 186 (100)) n (%)
Title
A combination of the labels ‘feasibility’ and/or ‘pilot’	142 (76.3)
Preliminary effectiveness’ and/or ‘preliminary efficacy’ and/or  ‘preliminary effect’ and/or ‘preliminary impact’	1 (0.5)
A formulation suggesting hypothesis testing	40 (21.5)
With the labels ‘feasibility’ and/or ‘pilot’	26 (14)
Without the labels ‘feasibility’ and/or ‘pilot’	14 (39.9)
Objectives
The labels ‘feasibility’ and/or ‘pilot’ and/or intervention refinement	69 (37.1)
A combination of the labels ‘feasibility’ and/or ‘acceptability’  paired with hypothesis testing	32 (17.2)
Calculate the sample size for the larger trial	1 (0.5)
A formulation suggesting hypothesis testing	84 (45.2)
Study design
The labels ‘feasibility’ and/or ‘pilot’	91 (48.9)
Other research design without the labels ‘feasibility’ and/or ‘pilot’	88 (47.3)
No study design mentioned	7 (3.8)
Type of design reported
Randomised	76 (40.9)
Quasi-experimental	68 (36.6)
Mixed	19 (10.2)
Observational	10 (5.4)
Qualitative	6 (3.2)
Sample size
Rationale to support sample size choice reported (1 or more  of the following reasons reported):	61 (32.8)
Power calculations	40 (21.5)
Sample size of previous feasibility studies	17 (9.1)
Expected effect size of an intervention	12 (6.5)
Qualitative data saturation	3 (1.6)
Based on feasibility study literature	11 (5.9)
No rationale to support sample size choice	125 (67.2)

In 91 papers (48.9%), the study design was characterised with the label ‘pilot’ or ‘feasibility’, 88 papers (47.3%) reported a study design without using the word ‘pilot’ or ‘feasibility’. Although the included papers had either ‘Pilot Projects’ and ‘Feasibility Studies’ in the title as per our search keywords, seven papers (3.8%) did not specify any study design within their main text. Among papers that described a design, descriptors included: ‘randomised’ (n = 76, 40.9%), ‘quasi-experimental’ (n = 68, 36.6%), ‘mixed’ (n = 19, 10.2%), ‘observational’ (n = 10, 5.4%) and ‘qualitative’ (n = 6, 3.2%).

The majority of papers (n = 125, 67.2%) did not report any rationale to support their sample size. The other papers (n = 61, 32.8%) reported one or several rationales, including power calculations (n = 40, 21.5%), sample size of previous feasibility studies (n = 17, 9.1%), expected effect size of an intervention (n = 12, 6.5%), various methodological references from the feasibility study literature (n = 11, 5.9%), or qualitative data saturation (n = 3, 1.6%). Among the methodological references identified in this review (Cocks and Torgerson, 2013; Feeley et al., 2009; Hertzog, 2008; Kraemer et al., 2006; Lancaster et al., 2004; Leon et al., 2011; Thabane et al., 2010), the confidence interval approach (degree of precision) was the most frequent (n = 4 (Eldridge et al., 2016a; Hertzog, 2008; Thabane et al., 2010)). Of note, sample sizes were increased in anticipation of attrition rates in 12 papers (6.5%).

Considering these characteristics and the codification scheme prepared for the analysis in this descriptive review, we observed that although most papers (n = 142, 76.3%) included the label ‘pilot’ or ‘feasibility’ in their title, reporting for the other components was generally not coherent with the reporting standards. As presented in Table 4, most papers reported objectives (n = 116, 62.4%), designs (n = 95, 51%), or rationales for sample size (n = 165, 88.7%) that were incongruent with the purpose of feasibility studies. These results did not differ when stratified by year of publication.

Table 4.

Congruency with reporting standards.

Design element	Papers congruenta with reporting standards	Papers presenting incongruent or incompletea reporting
	n (%)
Title	142 (76.3)	44 (23.7)
Objectives	70 (37.6)	116 (62.4)
Study design	91 (48.9)	95 (51)
Rationale for the sample size	21 (11.3)	165 (88.7)

^aBased on the definitions presented in Table 2.

Discussion

This systematic descriptive review appraised the reporting of 186 feasibility studies in the nursing intervention research literature and found that the majority did not adhere to one or several recommendations of the CONSORT statement extension for feasibility studies. The observations from this descriptive review result in two important contributions. First, our results highlight that this design remains misused as a large proportion of feasibility studies in the nursing intervention literature still focuses on hypothesis testing rather than on acceptability and feasibility of research protocols or interventions. Indeed, we observed that while most papers included the label ‘pilot’ or ‘feasibility’ in their title, the majority reported objectives, designs, or rationales for sample size that were not consistent with the purpose of feasibility studies. The purpose of feasibility studies is to investigate features of a research protocol or intervention that could hamper the success of a full-scale trial (Eldridge et al., 2016a, 2016b; Feeley and Cossette, 2015a, 2015b). Second, our results highlight the rationales used in nursing intervention feasibility studies to support the sample size. As guidance on the subject remains vague, highlighting the rationales that are coherent with the purpose of feasibility studies and the references to support these rationales will inform the nursing scientific community and may contribute to better practice in reporting feasibility studies.

Perhaps the most interesting but concerning finding from this review was the large number of papers misleadingly claiming to report on a feasibility study, when in fact they were presenting results regarding the effectiveness of an intervention. While all papers described feasibility studies, almost half of the papers focused exclusively on testing the effectiveness of an intervention. This goes against the purpose of the feasibility study design, which should not aim to test hypotheses regarding the effectiveness of an intervention (Arain et al., 2010). Another concerning finding was the lack of rationale to support sample sizes in more than 60% of the papers included in the review, and the presentation of power calculations to support sample sizes in 20% of the papers. This means that the vast majority of the papers reviewed did not follow the CONSORT recommendations regarding sample sizes, an issue that probably reflects misunderstandings regarding the purpose of the feasibility study design.

These results are consistent with previous reviews in which poor reporting of feasibility studies in various fields was highlighted (Arain et al., 2010; Eldridge et al., 2016a, 2016b; Lancaster et al., 2004). However, this is the first review to focus on the field of nursing intervention research. Considering the importance of intervention development and testing in nursing scholarship, it appears as a field conducive to the improvement of reporting practices for feasibility studies. However, the results of this review show that the recommendations for reporting feasibility studies have yet to have an impact in the field of nursing intervention research.

Nevertheless, some papers exemplified best practices for reporting feasibility studies. For example, Walker et al. (2015) reported on a protocol for a pilot study to evaluate the feasibility of study administration, resource and data management, intervention fidelity and effect size of a prophylactic dressing intervention to minimise sacral pressure. Verloo et al. (2016) investigated the feasibility and acceptability of delirium assessment methods in the context of home care for a randomised controlled trial. Cossette et al. (2017) assessed the feasibility, acceptability and preliminary efficacy of a nursing intervention to enhance patient acceptance of implantable cardioverter defibrillators. In all three cases, titles and designs clearly included the label ‘pilot’ or ‘feasibility’, objectives were congruent with the purpose of feasibility studies, and rationales for sample sizes are provided without involving power calculations for hypothesis testing.

Limitations

It could be argued that the timespan that this review covered was relatively close to the publication date of the recommendations; the review could be repeated in a few years to get a better sense of the impact that the recommendations had on the reporting of feasibility studies in nursing intervention research.

Conclusion and recommendations

In this descriptive review, we have systematically reviewed the literature on feasibility studies in nursing intervention research and appraised the reporting of characteristic components of feasibility studies in nursing intervention research, based on the CONSORT statement extension for feasibility studies. Our results highlight that the reporting of feasibility studies is still poor. This study design remains misused, as evidenced by the fact that a large proportion of feasibility studies in the nursing intervention research literature still focus on hypothesis testing.

Another objective was to identify the rationales and key references to support sample sizes in feasibility studies in nursing intervention research. We found that the confidence interval approach (degree of precision) was the most frequent. Additionally, key methodological references used by authors of feasibility studies in nursing intervention research, that are coherent with CONSORT recommendations, were highlighted in this review (Cocks and Torgerson, 2013; Feeley et al., 2009; Hertzog, 2008; Kraemer et al., 2006; Lancaster et al., 2004; Leon et al., 2011; Thabane et al., 2010).

Based on the results of this descriptive review, we would argue that there is a need for more sensitisation and education regarding the purpose, conduct and reporting of feasibility studies among the nursing scientific community, and that there is a need for more scrutiny of any manuscript that claims to report on a feasibility study.

Key points for policy, practice and/or research

• The results of this descriptive review highlight that the reporting of feasibility studies is still weak.

• The findings highlight that this study design remains misused, as evidenced by the fact that a large proportion of feasibility studies in the nursing intervention research literature still focus on hypothesis testing.

• In terms of rationale to support sample sizes in feasibility studies, this descriptive review found that the confidence interval approach (degree of precision) was the most frequent.

• Results of this descriptive review suggest that there is a need for more sensitisation and education regarding the purpose, conduct, and reporting of feasibility studies among the nursing scientific community.

Biography

Tanya Mailhot, RN, PhD, is a Postdoctoral Fellow at the Department of Pharmacy and Health Systems Sciences of the Bouvé College of Health Sciences at Northeastern University in Boston, Massachusetts. Her work focuses on cognition among elderly patients hospitalized in acute and critical care settings. She recently developed and assessed nursing interventions involving families in the management of postoperative delirium among elderly cardiac surgery patients.

Marie-Hélène Goulet, RN, PhD, is an Assistant Professor, Faculty of Nursing, Université de Montréal, Montreal, Canada. She is also a researcher at the Centre de recherche de l'Institut universitaire en santé mentale de Montréal and with the Quebec Network on Nursing Intervention Research. Her work focuses on the development, implementation and evaluation of interventions in psychiatric environments.

Marc-André Maheu-Cadotte, RN, BSN, is a Doctoral Candidate and a Quebec Healthcare Research Fund Scholar at the Faculty of Nursing of the University of Montreal. His research interests focus on cardiac patients’ health experiences and the practice of healthcare professionals in cardiovascular care. He also has a strong interest in the use of digital interventions to train healthcare professionals, such as serious games and virtual simulation.

Guillaume Fontaine, RN, MSN, is a Doctoral Candidate and Vanier Canada Graduate Scholar at the Faculty of Nursing of the University of Montreal. His main research interests are patient and healthcare professional behaviour change, and computer-based education. For his doctoral thesis, he investigates how behavioural science and data science can be used to develop and evaluate a theory-based, adaptive e-learning environment to support the implementation of brief behaviour change counselling in nurses’ clinical practice.

Pierre Lequin, RN, MSc, is a Clinician Nurse Specialist, at the Centre Hospitalier Universitaire Vaudois, Department of Psychiatry, in Lausanne, Switzerland.

Patrick Lavoie, RN, PhD, is an Assistant Professor, Faculty of Nursing, Université de Montréal, Montreal, Canada. He is also a researcher at the Montreal Heart Institute Research Center and with the Quebec Network on Nursing Intervention Research. His work focusses on nurses’ clinical judgment and decision-making in high-risk situations.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethics

Ethical permissions were not required for this work as it is a literature review and does not involve any participants.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by a postdoctoral fellowship from the Quebec Research Funds (Mailhot).

ORCID iD

Tanya Mailhot https://orcid.org/0000-0002-3156-4955