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. 2025 Dec 30;27:36. doi: 10.1186/s12875-025-03132-1

Screening tools for ruling out mood and anxiety disorders in adults in primary care: a rapid systematic review

Melissa Corso 1,2, Krystle Martin 2, Louis Rachid Salmi 3, Debbie S Wright 4, Jeffrey Quon 5, Carol Cancelliere 1, Silvano Mior 1,6, Kent Murnaghan 6, Pierre Côté 1,2,✉
PMCID: PMC12866250  PMID: 41466386

Abstract

Background

Patients with mood and anxiety disorders commonly visit primary care providers in Canada. Screening tools can support providers in identifying patients who need further mental health care.

Objectives

Identify screening tools that are valid and reliable for ruling out mood and anxiety disorders in adults in primary care settings.

Data sources

Our rapid review searched MEDLINE, Embase and PsycInfo from January 1, 2006, to May 27, 2025.

Study selection

A single reviewer conducted screening, critical appraisal and data extraction. Low risk-of-bias studies were included. Sensitivity, specificity, and negative likelihood ratios (NLRs) with 95% confidence intervals were extracted or calculated. A threshold of NLR < 0.1 was used to interpret strong rule-out performance.

Synthesis

We included 11 low risk-of-bias studies evaluating validity in 13 tools. No reliability studies were included due to high risk-of-bias. The Patient Health Questionnaire (PHQ)-2 (≥ 1/7), PHQ-9 (≥ 10/17), Generalised Anxiety Disorder (GAD)-2 (≥ 2/6), and GAD-7 (≥ 5/21) demonstrated the strongest evidence for ruling out depression and anxiety (NLR < 0.1).

Conclusions

The PHQ-2, PHQ-9, HADS-D, 15-item GDS, QIDS-SR16, GAD-2, and GAD-7 are brief, valid tools with strong rule-out performance for depression and anxiety in primary care. Future research should evaluate reliability and performance in diverse providers and patients, including non-physician settings and underserved communities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-025-03132-1.

Keywords: Screening tools, Mental health, Validity, Ruling out, Primary care

Background

In 2021, mental health disorders (MHDs) were the sixth leading cause of disability-adjusted life years, globally [1]. In 2022, 18% of Canadians aged 15 and older met the diagnostic criteria for a mood, anxiety, or substance use disorder [2]. The annual economic cost of MHDs including costs for healthcare, lost productivity and reduced health-related quality of life exceeds $50 billion [3]. However, only half of Canadians with MHDs have discussed their mental health with a health professional [2], and only 44% of those who needed counselling or psychotherapy received care.

In Canada, nearly a quarter of visits to general practitioners are for MHDs [4]. Furthermore, there is evidence that individuals who struggle with chronic illness and a MHD, such as depression, often have poorer health outcomes [5] and a higher risk of mortality [6]. Therefore, it is important for primary care providers to screen patients for anxiety or mood disorders and refer them to mental healthcare professionals when appropriate. However, clinicians may rely on intuition to detect MHDs or worse, fail to actively explore mental health due to barriers to conducting routine screening [7]. These include attitudes about MHD and patient responses to screening and treatment, clinician time constraints, inadequate training in the use of mental health screening tools, and inability to diagnose and treat MHDs [8, 9] While several screening tools are available [10], there is no consensus on which tools are best suited for primary care settings [11–13]. We conducted a systematic rapid review to identify valid and reliable screening tools to rule out mood and anxiety disorders in adults in primary care settings.

Methods

Protocol and registration

We conducted a rapid systematic review according to the World Health Organization [14] and Cochrane Rapid Review Methods [15] and registered our protocol with Open Science Framework Registries (osf.io/3mqdu). We report our systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist [16]. Clinical trial number: not applicable.

Data sources

A health sciences librarian designed the search strategy, which was reviewed according to the Peer Review of Electronic Search Strategies (PRESS) Checklist [17, 18] We searched MEDLINE, Embase and PsycInfo for articles in English and French from January 1, 2006 to August 24, 2023 and updated to May 27, 2025. The strategy included four concept groups: (1) primary care settings, (2) mood and anxiety disorders, (3) screening tools, and (4) validity and reliability. (Additional File 1)

Data availability

The dataset supporting the conclusions of this article are included within the article and its additional files.

Study selection

Table 1 outlines the study selection eligibility criteria. Details are defined in Additional File 2.

Table 1.

Study selection eligibility criteria

Population Adult participants recruited from primary care settings.
Exposure Screening tool for mood and anxiety disorders including questionnaires or clinical diagnostic methods.
Comparison Reference standard used to screen mood and anxiety disorders in adults.
Outcome Validity (content, face, construct, structural, and criterion validity), and test-retest, inter- and intra-rater reliability.
Study design Randomized controlled trials (RCTs), cohort, case-control and cross-sectional studies.
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We used EPPI-Reviewer as citation management and screening software [19] According to rapid review recommendations [14, 15], screening occurred in two phases: (1) title and abstract, and (2) full-text studies, by a single reviewer. Two independent investigators (MC and KM) screened a random 10% in each phase and exceeded 95% agreement before a single investigator (MC) screened the remaining studies. Any disagreements were brought to the research team.

Risk-of-bias

One trained investigator (MC) assessed the risk-of-bias which were reviewed by the research team. We used Risk-of-Bias 2 (ROB 2) tool for RCTs, [20] Quality Appraisal of Diagnostic Reliability Checklist (QAREL) for reliability studies, [21] and Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) for diagnostic accuracy studies [22].

Data extraction

One investigator (MC) extracted data from low risk-of-bias studies which was validated by the research team. We extracted study design, setting, sample size, index test, reference standard and psychometric properties including sensitivity (proportion of people with the condition who have a positive test) [23], specificity (proportion of people without the condition who have a negative test) [23], and negative likelihood ratio (NLR (probability of not having the condition when the test result is negative)) [23].

Statistical analyses

We calculated sensitivity, specificity and NLR with 95% confidence intervals when these statistics were not reported [24]. Contingency tables were recreated to calculate sensitivity and specificity [23] in Excel (Microsoft 365, Washington USA). NLR was calculated using a bootstrapping method [24] in R (version 4.2.2, R core team 2021) [25].

Evidence synthesis

We conducted a narrative synthesis of low risk-of-bias studies stratifying the synthesis by MHD and screening tool. We did not include high risk-of-bias studies in our synthesis because their methodological flaws likely led to biased result [26]. The results of our risk-of-bias assessment are reported in Additional File 3. We summarized patient population, range of sensitivity and specificity across cut-off scores and the lowest NLR with associated cut-off score in Table 2, with details in Additional File 4.

Table 2.

Results summary of sensitivity, specificity and NLR stratified by condition

Questionnaire Population Sensitivity Specificity Score Range NLR Score
Depression
 PHQ-2

• 16 years or older in medical family practices in Auckland, New Zealand [27]

• Over 65 years in primary care clinics in Seattle, Washington, USA [28]

 38–96% 60–98% 1–5/6 0.07 ≥1/6
 PHQ-9

• 16 years or older in medical family practices in Auckland, New Zealand [27]

• Over 65 years in primary care clinics in Seattle, Washington, USA [28]

• Adults with coronary heart disease in South London [29]

• Adults from a private primary care clinic in northern Singapore [30]

• Adults from three primary care centres in London, UK [31]

 45–95% 70–97% 10–15/17 0.07 ≥10/17
 HADS-D • Adults with coronary heart disease in South London, UK [29] 53–100% 57–92% 3–8/21 0.00 ≥3/21
 15-item GDS • Over 65 years in two primary clinics in Seattle, Washington, USA [28] 13–100% 15–93% 5–9/15 0.00 ≥5/15
 CAT-MDD • Adults from an urban internal medicine clinic in the USA [32] 77% 93% N/A 0.24 N/A
 CMFC • Adults from five primary care physician settings in Dallas, Fort Worth Metro-plex, USA [33]

In: 94%

SAM: 45%

In: 65%

SAM: 93%

 N/A

In: 0.09

SAM: 0.59

 N/A
 QIDS-SR16 • Adults from a private primary care clinic in northern Singapore [30] 83–94% 78–85% 7–9/27 0.05 ≥7/27
 PDI-4 • Adults across 21 primary care clinics in the USA [34, 35] 80% 80% N/A 0.25 N/A
 M-3 • Adults from a family medicine clinic at the University of North Carolina, USA [36] 84% 80% 5/32 0.20 ≥5/32
Bipolar Disorder
 CMFC • Adults from five primary care physician settings in Dallas, Fort Worth Metro-plex, USA [33]

In: 63%

SAM: 50%

In: 79%

SAM: 97%

 N/A

In: 0.48

SAM: 0.52

 N/A
 M-3 • Adults from a family medicine clinic at the University of North Carolina, USA [36] 88% 70% 2/32 0.17 ≥2/32
 MDQ • Adults from four primary care clinics at Howard University Hospital, Washington, DC, USA [37] 62% 69% N/A 0.55 N/A
Persistent Depressive Disorder
 CMFC • Adults from five primary care physician settings in Dallas, Fort Worth Metro-plex, USA [33]

In: 92%

SAM: 33%

In: 55%

SAM: 93%

 N/A

In: 0.15

SAM: 0.72

 N/A
Mania
 PDI-4 • Adults across 21 primary care clinics in the USA [34, 35] 83% 75% N/A 0.23 N/A
Anxiety
 GAD-2 • Adults from 15 primary care sites across 12 states in the USA, including 13 family practices and 2 internal medicine practices [24, 25] 86–95% 64–83% 2–3/6 0.09 ≥2/6
 GAD-7 • Adults from 15 primary care sites across 12 states in the USA, including 13 family practices and 2 internal medicine practices [24, 25] 48–97% 57–95% 5–15/21 0.05 ≥5/21
 CMFC • Adults from five primary care physician settings in Dallas, Fort Worth Metro-plex, USA [33]

In: 73%

SAM: 73%

In: 89%

SAM: 89%

 N/A

In: 0.10

SAM: 0.30

 N/A
 M-3 • Adults from a family medicine clinic at the University of North Carolina, USA [36] 82% 70% 3/32 0.17 ≥3/32
Panic Disorder
 GAD-2 • Adults from 15 primary care sites across 12 states in the USA, including 13 family practices and 2 internal medicine practices [24, 25] 76–91% 64–83% 2–3/6 0.09 ≥2/6
 GAD-7 • Adults from 15 primary care sites across 12 states in the USA, including 13 family practices and 2 internal medicine practices [24, 25] 74–94% 56–81% 5–10/21 0.11 ≥5/21
 CMFC • Adults from five primary care physician settings in Dallas, Fort Worth Metro-plex, USA [33]

In: 79%

SAM: 32%

In: 52%

SAM: 76%

 N/A

In: 0.41

SAM: 0.89

 N/A
 PDI-4 • Adults across 21 primary care clinics in the USA [34, 35] 88% 68% N/A 0.89 N/A
Social Anxiety Disorder
 GAD-2 • Adults from 15 primary care sites across 12 states in the USA, including 13 family practices and 2 internal medicine practices [24, 25] 70–85% 62–81% 2–3/6 0.24 ≥2/6
 GAD-7 • Adults from 15 primary care sites across 12 states in the USA, including 13 family practices and 2 internal medicine practices [24, 25] 72–88% 55–80% 5–10/21 0.21 ≥5/21
 CMFC • Adults from five primary care physician settings in Dallas, Fort Worth Metro-plex, USA [33]

In: 92%

SAM: 42%

In: 55%

SAM: 75%

 N/A

In: 0.16

SAM: 0.76

 N/A
Specific Phobias
 CMFC • Adults from five primary care physician settings in Dallas, Fort Worth Metro-plex, USA [33]

In: 68%

SAM: 42%

In: 51%

SAM: 75%

 N/A

In: 0.60

SAM: 0.76

 N/A
Social Phobia
 PDI-4 • Adults across 21 primary care clinics in the USA [34, 35] 57% 70% N/A 0.61 N/A
Mood and Anxiety Disorders
 PCBHS • Adults from an outpatient family practice Saint Louis, Missouri, USA [23] 14–81% 46–100% ≥0–3 0.42 ≥0
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Our review focuses on NLR because it is the most appropriate statistical measure of a tool’s ability to rule out a condition. In other words, the NLR provides an indication to primary care clinicians of the utility of a negative test and the smaller the NLR, the more confident a clinician can exclude the disease. A NLR less than 1 indicates that a test score is associated with the absence of the condition. The closer the NLR is to 0, the greater confidence we can have in the ability of the tool to rule out mood or anxiety disorders when the score is below the cut-off. We used NLR<0.1 as a threshold when considering strong performance for ruling out mood or anxiety disorders [26]. We created forest plots to summarize the lowest NLRs across studies and tools.

Results

Study selection

We identified 6,690 citations (600 duplicates) (Figure 1) in phase 1 and 155 studies in phase 2. Two rounds of dual screening occurred prior to reaching agreement. Inter-rater percent agreement was 93.6% in round 1 and 96% in round 2. We identified 26 relevant studies, two pairs reporting from the same dataset [38–41]. We identified an additional 1,756 citations (302 duplicates) in the search update, 22 were screened in phase 2. No relevant studies were added. The primary aim of all studies included in our review was to assess the validity and reliability of screening tool.

Fig. 1.

Fig. 1

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PRISMA flow diagram for study selection and inclusion

Risk-of-bias

We evaluated the risk-of-bias of 23 studies. Methodological limitations of high risk-of-bias studies included lack of blinding [34, 35, 38, 39, 42–47], lack of appropriate interval between tests [27–29, 32, 34, 35, 42, 44, 46, 47], inadequate sampling [30, 34, 35], and not analyzing data from the full sample [27–30, 32, 34, 35, 43–47]. (Additional File 3) Of 23 eligible articles, 13 had low risk-of-bias and were included in the synthesis. Two pairs of studies report on the same dataset [38–41], therefore, our rapid review reports on 11 studies.

Study characteristics

Six studies investigated the Patient Health Questionnaire-9 (PHQ-9) [31, 33, 36, 37, 48, 49], three investigated the Patient Health Questionnaire-2 (PHQ-2) [31, 33, 37], one investigated each of the Generalised Anxiety Disorder 7-item (GAD-7) [38, 39], Generalised Anxiety Disorder 2-item (GAD-2) [38, 39], Computerized Adaptive Test for Mental Health (CAT-MH) [33], Hospital Anxiety and Depression Scale – Depression Subscale (HADS-D) [36], 15-item Geriatric Depression Scale (GDS) [37], Connected Mind Fast Check (CMFC) [50], Quick Inventory of Depressive Symptomatology (16-item) (QIDS-SR16) [48], My Mood Monitor (M-3) [51], Mood Disorder Questionnaire (MDQ) [52], Provisional Diagnostic Instrument-4 (PDI-4) [40, 41], and Primary Care Behavioural Health Screen (PCBHS)[42]. Six tools (PHQ-9, PHQ-2, HADS-D, 15-item GDS, QIDS-SR16 and MDQ) screened for depression, two (GAD-7 and GAD-2) screened for anxiety, and five (CAT-MH, CMFC, M-3, PDI-4, and PCBHS) screened for different mood and anxiety disorders. The CAT-MH screens for both mood and anxiety disorders, but only reported psychometric properties for mood disorders [33]. The PCBHS did not differentiate between mood and anxiety disorders in its analysis [53].

The PHQ-2, PHQ-9, GAD-2, GAD-7, CAT-MH, CMFC, QIDS-SR16, M-3, PDI-4 and PCBHS were assessed for adults seeking care in primary care clinics [31, 33, 38–42, 48, 50, 51], the PHQ-2, PHQ-9 and 15-item GDS were also assessed in older adults (≥65 years) [37], the PHQ-9 and HADS-D were tested in patients with coronary heart disease [36], the PHQ-9 was tested in patients recently diagnosed with Type 2 diabetes [49] and the MDQ was tested in patients who experienced significant trauma [52].

Screening tools for mood disorders

As a reminder, our review focussed on the NLR which provides an indication of the utility of negative test. The smaller the NLR of a test, the more confidently a clinician can exclude the presence of the disease.

Depression

The PHQ-2’s lowest NLR was 0.07 (95% CI [0.03, 0.13]) when utilizing a cut-off score ≥1/6 [31]. The PHQ-9’s lowest NLR was 0.07 (95% CI [0.02, 0.15]) with a score ≥10/17 [49]. The HADS-D lowest NLR was 0.00 (95% CI [0.00, 0.20]) with a score ≥3/21. [36] The 15-item GDS’ lowest NLR was 0.00 (95% CI [0.00, 0.56]) with a score ≥5/15.[37] The CAT-MDD had a NLR of 0.24 (95% CI [0.10, 0.42]), but no corresponding cut-off score was reported [33]. The CMFC initial screen (CMFC-IS) had a NLR of 0.09 (95% CI [0.00, 0.22]) and the specific assessment module (CMFC-SAM) had a NLR of 0.59 (95% CI [0.44, 0.74]) [50]. The QIDS-SR16 had a NLR of 0.05 (95% CI [0.00, 0.19]) with a score ≥7/27 [48]. The PDI-4 had a NLR of 0.25 (95% CI [0.16, 0.37]) for major depressive episodes with no score reported [40, 41]. The M-3 had a NLR of 0.20 (95% CI [0.13, 0.28]) with a score ≥5/32 [51] Fig 2. (Figure 2: Forest plot summarizing lowest NLR (95% CI) of each study for depression).

Fig. 2.

Fig. 2

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Forest plot summarizing lowest NLR (95% CI) of each study for depression

Tools that reached the threshold of 0.1 included the PHQ-2, PHQ-9, HADS-D, 15-item GDS and the QIDS-SR16. Only the PHQ-2 and PHQ-9 had multiple studies investigating psychometric properties.

Bipolar disorder

The CMFC-IS had a NLR of 0.48 (95% CI [0.18, 0.79]) and the CMFC-SAM had a NLR of 0.52 (95% CI [0.26, 0.77]) [50]. The M-3 had a NLR of 0.17 (95% CI [0.07, 0.30]) with a score ≥2/27 [51]. The MDQ had a NLR of 0.55 (95% CI [0.26, 0.87]) with no score provided [52] Fig 3. (Figure 3: Forest plot summarizing lowest NLR (95% CI) of each study for bipolar disorder).

Fig. 3.

Fig. 3

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Forest plot summarizing lowest NLR (95% CI) of each study for bipolar disorder

Other mood disorders

The CMFC-IS for persistent depressive disorder had a NLR of 0.15 (95% CI [0.00, 0.53]) and the CMFC-SAM had a NLR of 0.71 (95% CI [0.40, 0.98]) [50]. The PDI-4 for mania had a NLR of 0.23 (95% CI [0.13, 0.34]) [40, 41] Fig 4. (Figure 4: Forest plot summarizing lowest NLR (95% CI) of each study for other mood disorders).

Fig. 4.

Fig. 4

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Forest plot summarizing lowest NLR (95% CI) of each study for other mood disorders

Screening tools for anxiety disorders

Generalized anxiety disorder

The GAD-2 had a NLR of 0.09 (95% CI [0.02, 0.18]) with a score ≥2/6 [38, 39]. The GAD-7’s lowest NLR was 0.05 (95% CI [0.00, 0.13]) with a score ≥5/21 [38, 39]. The CMFC-IS had a NLR of 0.10 (95% CI [0.03, 0.22]) and the CMFC-SAM had a NLR of 0.30 (95% CI [0.03, 0.22]) [50]. The PDI-4 had a NLR of 0.22 (95% CI [0.06, 0.45]) [40, 41]. The M-3 had a NLR of 0.17 (95% CI [0.07, 0.30]) with a score ≥3/32 [51]. The GAD-2, GAD-7 and CMFC-IS all reached the threshold for ruling out GAD Fig 5. (Figure 5: Forest plot summarizing lowest NLR (95% CI) of each study for generalized anxiety disorder)

Fig. 5.

Fig. 5

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Forest plot summarizing lowest NLR (95% CI) of each study for generalized anxiety disorder

Panic disorder

The GAD-2 had a NLR of 0.09 (95% CI [0.02, 0.18]) with a score ≥2/6 [38, 39]. The GAD-7 had a NLR of 0.11 (95% CI [0.03, 0.23]) with a score ≥5/21 [38, 39]. The CMFC-IS had a NLR of 0.41 (95% CI [0.14, 0.74]) and the CMFC-SAM had a NLR of 0.89 (95% CI [64, 1.12]) [50]. The PDI-4 had a NLR of 0.17 (95% CI [0.05, 0.34]) for panic attacks in adults [40, 41]. Only the GAD-2 reached the threshold for ruling out panic disorder Fig 6. (Figure 6: Forest plot summarizing lowest NLR (95% CI) of each study for panic disorder)

Fig. 6.

Fig. 6

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Forest plot summarizing lowest NLR (95% CI) of each study for panic disorder

Social anxiety disorder

The GAD-2’s lowest NLR was 0.24 (95% CI [0.11, 0.41]) with a score ≥3/6 [38, 39]. The GAD-7 had a NLR of 0.21 (95% CI [0.09, 0.38]) at a score ≥5/21 [38, 39]. The CMFC-IS had a NLR of 0.16 (95% CI [0.00, 0.42]) and the CMFC-SAM had a NLR of 0.76 (95% CI [0.48, 1.03]) [50] Fig 7. (Figure 7: Forest plot summarizing lowest NLR (95% CI) of each study for social anxiety disorder).

Fig. 7.

Fig. 7

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Forest plot summarizing lowest NLR (95% CI) of each study for social anxiety disorder

Other anxiety disorders

The CMFC-IS had a NLR of 0.60 (95% CI [0.25, 1.02]) for specific phobias in adults while the CMFC-SAM had a NLR of 0.76 (95% CI [0.47, 1.03]) [50]. The PDI-4 had a NLR of 0.61 for social phobia in adults (95% CI [0.38, 0.70]) [40, 41] Fig 8. (Figure 8: Forest plot summarizing lowest NLR (95% CI) of each study for other anxiety disorders).

Fig. 8.

Fig. 8

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Forest plot summarizing lowest NLR (95% CI) of each study for other anxiety disorders

Screening tools for mood and anxiety disorders

The PCBHS included both mood and anxiety disorders in the screening tool but did not define or differentiate them in their analysis [42]. The lowest NLR, 0.42 (95% CI [0.09, 0.93]), occurs when both a score ≥0 for the first question and a severity of symptoms rating greater than “somewhat” on the second question are present [42, 54] Fig 9. (Figure 9: Forest plot summarizing lowest NLR (95% CI) of each study for both mood and anxiety disorders).

Fig. 9.

Fig. 9

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Forest plot summarizing lowest NLR (95% CI) of each study for both mood and anxiety disorders

Discussion

Our systematic rapid review aimed to identify valid and reliable screening tools to rule out mood and anxiety disorders in adults in primary care settings. We identified 13 screening tools in 11 low risk-of-bias studies to rule out mood or anxiety disorders in primary care. Our review suggests the PHQ-2 (≥1/6) and PHQ-9 (≥10/17), HADS-D (≥3/21), 15-item GDS (≥5/15) and the QIDS-SR16 (≥7/27) for depression, the GAD-2 (≥3/6), GAD-7 (≥5/21) and CMFC-IS, for anxiety, and the GAD-2 (≥2/6) for panic disorder may assist primary care clinicians in ruling out these conditions. However, no tool reached the pre-specified threshold for ruling out bipolar disorder, persistent depressive disorder, mania, social anxiety disorder specific or social phobias. Approximately 20% of people with a MHD also have a substance use disorder, and people with a substance use disorder are three times more likely to have a MHD [55]. Further, MHDs and substance use disorders show specific disparities across gender, age, Indigenous identity, and socioeconomic status [56, 57]. The tools in our review were not assessed in patients with known co-occurrence of substance use disorder, or in patients with specific demographic or socioeconomic status’, therefore, the ability of the reported screening tools to rule out MHD in specific populations is unknown.

Previously, El-Den et al. identified 55 different depression screening tools [11]. However, did not conduct a risk-of-bias assessment of relevant studies [11]. Miller et al. identified 40 depression self-administered screening tools in primary care; [12] we build on this by including more recent studies and tools for other mood and anxiety disorders. Our study innovates by focussing on a screening tool’s ability to rule out mood and anxiety disorders. The NLR provides evidence on the level of confidence that a patient with a ‘negative’ score (less than the cut-off score) does not have the mood or anxiety disorder. For clinicians, this provides a quantifiable measure to determine whether a MHD is, on the one hand, unlikely to be present, or, on the other hand, justifies consideration for referral to a mental health professional. For example, if a primary care clinician uses the PHQ-2 to screen for depression and the patient scores below the threshold score (1/6), then the clinician has increased confidence that the patient does not have depression. If a patient scores above the threshold, a primary care clinician should follow-up with further assessment to determine if the patient meets the diagnostic criteria for depression and if referral to additional resources is needed [58].

Primary care providers play an important role in the early detection of MHDs, however several barriers to implementation of screening tools have previously been identified [8]. This demonstrates that integrating screening tools into clinical practice requires additional considerations regarding acceptability of screening by both patients and professionals and upskilling for clinicians. First, practical trade-offs of incorporating tools, such as the mental and time-related burden on patients and clinicians, need to be considered. Shorter tools, such as the PHQ-2 or GAD-2, may improve use but sacrifice depth of assessment. Secondly, while using low threshold cut-off scores minimizes the risk of missing individuals with MHDs, it may classify individuals as being “at risk” even if they do not require intervention. A survey of family physicians in Saskatchewan identified that the majority of respondents were interested or very interested in identifying or treating MHDs, but less than half were satisfied or very satisfied with the mental health care they were able to deliver [59]. A critical aspect of the role of a primary care provider involves determining the appropriate course of action following screening. Indeed, it is important to emphasize that screening is never solely the application of a test but must include a comprehensive strategy regarding subsequent management of patients with a positive test. It is essential to determine when a patient’s mental health status can be monitored (mild or sub-clinical condition) or when to refer them for further investigations. Referring all patients with a positive test, whether they have mild/sub-clinical disease or are false positives, may overwhelm the healthcare system with unnecessary referrals or needlessly medicalize “normal” distress. Further, application of a screening test will imply the occurrence of false negative results, of which consequences must be considered in the screening strategy.

We recommend that primary care providers become familiar with the screening tools identified in our review and develop processes when the results suggest initiation of deeper discussions with patients about the best next steps [60]. Implementing and understanding the results of mental health screening tools can assist the clinician in practicing clinical judgment to detect potential depression or anxiety, and decide on further steps for each patient.

Strengths and limitations

Our rapid review has limitations, including the restricted date range of the search strategy and one reviewer conducting screening and critical appraisal of studies. We did not conduct a sensitivity analysis to include high risk-of-bias in the analysis. Critical reasons for being rated as high risk-of-bias included not having a consecutive or random sample, no blinding of test interpretation, lack of appropriate intervals between tests, and not all participants receiving the reference standard or being included in the analysis. Therefore, including these studies in the evidence synthesis would not improve the validity of the results.

Strengths include a comprehensive search strategy using the PRESS Checklist, critical appraisal to report on low risk-of-bias studies, calculation of psychometric properties to compare tools using the same key outcomes and regular quality appraisal checkpoints at each stage of the rapid review process.

Conclusion

We found evidence that the PHQ-2, PHQ-9, HADS-D, 15-item GDS, QIDS-SR16, GAD-2, and GAD-7 may assist primary care clinicians in ruling out mood and anxiety disorders. Future research should consider assessing these mental health screening tools in underserved populations and in a variety of primary care settings including nurse practitioner, physiotherapy and chiropractic settings.

Supplementary Information

12875_2025_3132_MOESM1_ESM.docx (18.6KB, docx)

Additional File 1: MEDLINE search strategy.

12875_2025_3132_MOESM2_ESM.docx (25.4KB, docx)

Additional File 2: Detailed eligibility criteria.

12875_2025_3132_MOESM3_ESM.docx (37.2KB, docx)

Additional File 3: Risk-of-bias table of all relevant studies.

12875_2025_3132_MOESM4_ESM.docx (85.8KB, docx)

Additional File 4: Detailed evidence table stratified by questionnaire.

Acknowledgements

Not applicable.

Abbreviations

CAT-MH

Computerized Adaptive Test for Mental Health

CMFC

Connected Mind Fast Check

CMFC-IS

Connected Mind Fast Check Initial Screen

CMFC-SAM

Connected Mind Fast Check specific assessment module

GAD-2

Generalised Anxiety Disorder 2-item

GAD-7

Generalised Anxiety Disorder 7-item

GDS-15

15-item Geriatric Depression Scale

HADS-D

Hospital Anxiety and Depression Scale – Depression Subscale

M-3

My Mood Monitor

MDQ

Mood Disorder Questionnaire

MHDs

mental health disorders

NLR

negative likelihood ratio

PCBHS

Primary Care Behavioural Health Screen

PDI-4

Provisional Diagnostic Instrument-4

PHQ-2

Patient Health Questionnaire-2

PHQ-9

Patient Health Questionnaire-9

QAREL

Quality Appraisal of Diagnostic Reliability Checklist

QIDS-SR16

Quick Inventory of Depressive Symptomatology (16-item)

QUADAS-2

Quality Assessment of Diagnostic Accuracy Studies

RCTs

randomized controlled trials

ROB2

Risk-of-bias 2 tool

Authors’ contributions

MC prepared the systematic rapid review protocol, conducted all phases of the rapid review in full, prepared the manuscript and implemented feedback from all team members. KM was a content expert on mental health disorders, reviewing and providing feedback on the protocol, was the second reviewer for quality assurance steps and reviewed the manuscript for feedback. LRS was a content expert on epidemiology and diagnostic tests, reviewing and providing feedback on the protocol, conduct of the systematic rapid review and manuscript. DSW was a clinical advisor, providing feedback on clinical considerations and relevance of the protocol, results and manuscript. JQ was a content expert and advisor on epidemiology and clinical considerations of the protocol, results and manuscript. KM was the health sciences librarian, preparing the search strategy in collaboration with the research team, coordinating the PRESS review, implementing feedback and conducting the literature search. CC, SM and PC are senior scientists on the project, overseeing all aspects of the systematic rapid review protocol, conduct and manuscript.

Funding

This study was supported through funding by the College of Chiropractors of British Columbia to Ontario Tech University. The College of Chiropractors of British Columbia was not involved in the design, conduct or interpretation of the research that informed the research. Pierre Côté is supported by the Hann-Kelly Family Chair in Disability and Rehabilitation Research and the Research Excellence Chair in Musculoskeletal Rehabilitation at Ontario Tech University. Carol Cancelliere is supported by the Canadian Chiropractic Guideline Initiative.

Data availability

The dataset supporting the conclusions of this article are included within the article and its additional files.

Declarations

Ethics approval and consent to participate

This project did not require ethics approval, no submission was made to the Ontario Tech Research Ethics Board.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12875_2025_3132_MOESM1_ESM.docx (18.6KB, docx)

Additional File 1: MEDLINE search strategy.

12875_2025_3132_MOESM2_ESM.docx (25.4KB, docx)

Additional File 2: Detailed eligibility criteria.

12875_2025_3132_MOESM3_ESM.docx (37.2KB, docx)

Additional File 3: Risk-of-bias table of all relevant studies.

12875_2025_3132_MOESM4_ESM.docx (85.8KB, docx)

Additional File 4: Detailed evidence table stratified by questionnaire.

Data Availability Statement

The dataset supporting the conclusions of this article are included within the article and its additional files.

The dataset supporting the conclusions of this article are included within the article and its additional files.

Articles from BMC Primary Care are provided here courtesy of BMC

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