Integrating social paediatrics through experiential and advocacy‐based learning
Bibliographic record
Abstract
Our data indicated that panel co-management significantly improved rPCP's perceptions of the clinic experience and continuity of care without diminishing the extent to which rPCPs felt like the PCP for their patients.Prior to the intervention, 77% of rPCPs felt overwhelmed to extremely overwhelmed by inter-visit communication during inpatient rotations, which decreased to 35% post-intervention.Additionally, rPCPs felt panel co-management improved continuity of care for patients.The feedback received from this active collaboration of rPCPs and PAs taught us that PA and rPCP co-management helped address the fundamental difference in access to and continuity of care for patients of rPCPs, as evidenced by the improvements perceived by rPCPs in response to and triage of inbasket messages, scheduling with the PA, and overall continuity of care.Importantly, the co-management model did not change the extent rPCPs felt like their patients' primary care physician, which is critical to preserve PCP relationships with and ownership of the patients on their panel, a formative part of primary care practice.Support of clinic and residency leadership was critical to initiating the intervention.This program can serve as a model for other clinics where residents provide longitudinal care to support both the resident and their patients by balancing between the PCP role and concomitant inpatient responsibilities and enhancing team-based 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".