The Academic Support Process (ASP) website: Helping preceptors develop resident learning plans and track progress
Bibliographic record
Abstract
BACKGROUND: At times, preceptors struggle with aspects of resident education. Many are looking for more support and faculty development in this area. AIMS: To address preceptors' needs for resources and provide a proactive framework for their teaching, the Academic Support Process (ASP) website was developed and evaluated. Preceptors' (N = 35) experiences using the ASP website, as well as their perceptions of its usefulness in supporting resident education, were identified. METHODS: The research comprised two phases: a self-directed workshop involving the creation of a web-based learning plan for a standardised scenario of a resident in difficulty followed by 3 months use of the ASP website with residents in their practice. Information on their experiences was solicited via surveys and focus group interviews. RESULTS: Findings revealed the ASP website enabled preceptors to find words for their concerns around resident competency, gave them a proactive teaching framework, expanded their arsenal of teaching strategies, and supported a customised approach for all learners along the performance spectrum. However, there were a number of challenges encountered by the preceptors that affected site use and buy in. CONCLUSIONS: Results are promising. Next steps involve developing a clear strategy for adoption.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".