The influence of primary care physicians’ mental health knowledge, attitudes and self-efficacy on referrals to specialised services: findings from a longitudinal pilot trial
Bibliographic record
Abstract
BACKGROUND: Training based on the Mental Health Gap Action Programme (mhGAP) is being increasingly adopted by countries to enhance non-specialists' mental health capacities. However, the influence of these enhanced capacities on referral rates to specialised mental health services remains unknown. AIMS: We rely on findings from a longitudinal pilot trial to assess the influence of mental health knowledge, attitudes and self-efficacy on self-reported referrals from primary to specialised mental health services before, immediately after and 18 months after primary care physicians (PCPs) participated in an mhGAP-based training in the Greater Tunis area of Tunisia. METHOD: Participants included PCPs who completed questionnaires before (n = 112), immediately after (n = 88) and 18 months after (n = 59) training. Multivariable analyses with linear mixed models accounting for the correlation among participants were performed with the SAS version 9.4 PROC MIXED procedure. The significance level was α < 0.05. RESULTS: Data show a significant interaction between time and mental health attitudes on referrals to specialised mental health services per week. Higher scores on the attitude scale were associated with more referrals to specialised services before and 18 months after training, compared with immediately after training. CONCLUSION: Findings indicate that, in parallel to mental health training, considering structural/organisational supports to bring about a sustainable change in the influence of PCPs' mental health attitudes on referrals is important. Our results will inform the scale-up of an initiative to further integrate mental health into primary care settings across Tunisia, and potentially other countries with similar profiles interested in further developing task-sharing initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".