Scope: A new service supporting family doctors dealing with psychiatric patients in the community: Current utilization and quality improvement implementation protocol in the covid era
Bibliographic record
Abstract
Introduction Seamless Care-Optimizing Patients Experience-Mental Health (SCOPE-MH) is a hub-based integrative case management and psychiatric care program supporting family physicians (FPs). SCOPE-MH provides patient resource navigation, social support, counselling, psychiatric consults, and short-term follow-up. Due to COVID-19, SCOPE-MH pivoted to serve patients completely online. Objectives To assess current utilization and evaluate patients’ and FPs’ experiences using SCOPE-MH as an online service before and during COVID-19. Methods This evaluation was developed under the RE-AIM framework (Reach, Adoption, Implementation and Maintenance). Two surveys, one for Patient Reported Experience Measures (PREMS), and one seeking FPs perspective on the service, will complement the evaluation. Results Past data showed that 66.4% of referrals to SCOPE-MH were women (ages 14-97), and 33.6% were men (ages 14-91). The most common diagnoses were anxiety and depression, followed by adjustment reaction and PTSD. 72% of referred patients had more than one psychiatric diagnosis. 35.4% of the referrals were resource navigation and brief coordination of care. 39.2% required long term involvement. The main recommendations provided were counselling resources in the community and referral to local community mental health teams. Data on patient and FP experiences using SCOPE-MH, and perspectives on unique needs for psychiatric care in COVID-19, is still being collected. Surveys will be sent within 6 months. Conclusions SCOPE-MH is an effective model to support FP’s addressing patients’ psychiatric needs. The information obtained from the evaluation will be used to modify the online service to address unmet needs during COVID-19 and optimize current resources to serve more patients.
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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 | 0.135 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".