Psychiatric consultation
Bibliographic record
Abstract
OBJECTIVE: To further understand and optimize primary care provider (PCP) referrals to a 1-time psychiatric consultation service by developing profiles of PCP referrers, assessing PCP satisfaction with the service, and determining intervention opportunities. DESIGN: Secondary analysis of a referral database and subsequent cross-sectional survey of referrers. SETTING: Winnipeg, Man. PARTICIPANTS: All family physicians who had made at least 1 referral in 2017 to the Centralized Psychiatric Consultation Service for Adults, a 1-time consultation service. MAIN OUTCOME MEASURES: Referral frequency, individual and practice characteristics, satisfaction with the Centralized Psychiatric Consultation Service for Adults, and subjective drivers of referral activity were assessed. Interest in a range of intervention opportunities to increase mental health knowledge and support were also examined. RESULTS: <.001) were significantly associated with being a high referrer. Roughly 26.3% of low referrers, 29.2% of moderate referrers, and 15.4% of high referrers were satisfied with wait times for the service. Higher referrers did not identify a lack of comfort with providing psychiatric care as a driver of referrals; more indicated that they had a high volume of patients with mental health needs, that there was a lack of access to alternative services, and that patients sometimes requested referral. Overall, more than 40% of respondents expressed interest in a mental health care navigator, hard-copy resource information, and rapid access to consultation advice via telephone or an electronic platform. There was less interest in other proposed interventions. CONCLUSION: We found referrers to the Centralized Psychiatric Consultation Service for Adults to be clustered based on specific practice characteristics, as well as provider-patient factors. Overall, satisfaction with the service was fair and PCPs were not highly interested in a variety of proposed interventions. Future studies should explore how useful 1-time consultation services are for solo-practising PCPs and how best to support these and other PCPs in their management of patients with mental health needs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.257 | 0.046 |
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".