Exploring family physicians’ mental health referrals via centralized referral systems in Quebec, Canada: a qualitative descriptive study
Bibliographic record
Abstract
Background Centralized referral mechanisms anchored in primary care have been implemented to facilitate timely and appropriate access to health care in Quebec, Canada, like via Centre de répartition des demandes de services (CRDS), a regionally centralized referral system used by family physicians (FPs) for new requests to specialty care, including psychiatric services. CRDS for psychiatry was implemented in 2019, where local centralized referral systems to psychosocial or psychiatric services ( Guichets d’accès en santé mentale adulte (GASMA)) were already operating. We aimed to: 1) explore FPs’ use of CRDS for psychiatry; 2) better understand the functioning and potential complementarity of CRDS and GASMA, including by visually mapping these pathways; and 3) identify factors that may influence their use and functioning. Methods A qualitative descriptive study with 20 participants working in the healthcare sector was conducted. Thematic analysis was employed. Results Mental health referral pathways were mapped, with FPs as focal points. Factors identified as influencing referral mechanisms’ use and functioning included: 1) challenges related to the communication of ministerial directives on the use/functioning of centralized referral systems; 2) stakeholders’ perceptions on the regionally centralized system’s objectives for service access; 3) collaborations between clinicians and the regionally centralized system; 4) perceived added value of the regionally centralized system compared to pre-existing centralized local referral systems; and 5) key organizational/system-level mental health challenges and facilitators. Recommendations to improve these pathways’ use, functioning, and complementarity included clarifying directives and roles within trajectories, and improving communication between the regionally centralized and local systems already in place, as well as addressing organizational/system-level challenges to mental health care. Conclusions Findings inform on improvements for mental health service access, delivery, and continuity via centralized referral systems anchored in primary care and used by FPs; and access challenges via pathways and solutions to optimize mental health service trajectories.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".