How to be a good clerk on the clinical teaching team: a scoping review
Bibliographic record
Abstract
Background: As medical institutions shift towards Competency Based Education, more effort is being directed towards understanding how healthcare teams' function competently. While many have studied the competencies required to be a successful clerk, few have examined this question within the context of team function and integration. Our primary objective is to identify how medical clerks successfully integrate and contribute to clinical teaching teams. Methods: We performed a scoping review of the literature using the Ovid MEDLINE database. Data was extracted and thematically analysed in accordance with Arksey and O'Malley's (2005) approach to descriptive analysis. Results: Out of 1368 papers returned by our search, 12 studies were included in this review. Seven main themes were identified amongst the included studies: (1) Communication (2) Taking Responsibility and Appropriate Autonomy (3) Humility and Knowing When to Ask for Help (4) Identity as a Team Member, (5) Self-Efficacy (6) Rapport and Relationship Building (7) Patient Advocacy. Conclusion: Analysis of these themes revealed four major findings: (i) The importance of documentation skills and communication towards team contribution (ii) The important connection between professional identity development and self-efficacy (iii) The impact of rapport on the reciprocity of trust between team members (iv) The role of clerks as patient advocates is poorly understood. This review also illustrates that there is a relative dearth of literature in this area. Future studies are needed to develop clear guidance on how clerks should perform these competencies in the context of team function and integration.
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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 | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".