Mental health care in the primary care setting: family physicians' perspectives.
Bibliographic record
Abstract
OBJECTIVE: To assess family physicians' interactions with mental health professionals (MHPs), their satisfaction with the delivery of mental health care in primary health care settings, and their perceptions of areas for improvement. DESIGN: Mailed survey. SETTING: Province of Saskatchewan. PARTICIPANTS: All FPs in Saskatchewan (N = 816) were invited to participate in the study; 31 were later determined to be ineligible because they were specialist physicians, were no longer practising regularly, or could not be located. MAIN OUTCOME MEASURES: Family physicians' self-reported satisfaction with and interest in mental health care; perceived strengths and areas for improvement in the quality of mental health care delivery in primary health care settings. RESULTS: The response rate was 48%, with 375 FPs completing the survey. More than half of the responding FPs (56%) reported seeing 11 or more patients with mental health problems per week. Although 83% of responding FPs were interested or very interested in identifying or treating mental health problems, fewer than half (46%) reported being satisfied with the mental health care they were able to deliver. Family physician satisfaction was significantly higher among those with on-site MHPs (P < .05) and those who saw fewer patients with mental health problems per week (P < .01). The most common mode of interaction that FPs reported having with MHPs was through written correspondence; somewhat less common were telephone and face-to-face interactions. The most common strength FPs identified in their provision of mental health care was having access to psychiatrists, community mental health nurses, and other MHPs. The most common area for improvement in primary mental health care also fell under the category of access. Specifically, FPs felt access to psychiatrists needed to be improved. CONCLUSION: Mental health problems are very common in primary care. Most FPs are very interested in the detection and treatment of mental health problems. Despite this high level of interest, however, FPs are generally dissatisfied with the quality of mental health care they are able to provide. Access to MHPs was cited as a critical element in improving the delivery of mental health services in primary care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".