Caring for seriously mentally ill patients. Qualitative study of family physicians' experiences.
Bibliographic record
Abstract
OBJECTIVE: To examine family physicians' experiences in caring for patients with serious mental illness and their expectations of a shared mental health care (SMHC) model. DESIGN: Qualitative method of in-depth interviews. SETTING: London, Ont. PARTICIPANTS: Purposive sample of 11 full-time family physicians providing ongoing care for patients with serious mental illness. METHOD: Eleven interviews were conducted to explore family physicians' experiences. All interviews were audiotaped and transcribed verbatim. Analysis was done using a constant comparative approach and was carried out concurrently rather than sequentially. Researchers read all interview transcripts independently before comparing and combining their analyses. Final analysis involved examining all interviews together to discover relationships between and among emerging themes. MAIN FINDINGS: Findings reflected three main themes: what family physicians perceive they bring to care of seriously mentally ill patients (i.e., whole-person approach to care); challenges family physicians face in participating in shared care of these patients (i.e., communication and access issues); and family physicians' expectations of a SMHC model (i.e., guidance and feedback). CONCLUSION: As seriously mentally ill patients are moved out of institutions, the need for an effective and efficient SMHC model becomes imperative. Our findings suggest that family physicians could be an important part of SMHC models but only if systemic barriers are removed and collaborative practice is encouraged.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".