CORE UNDERGRADUATE OPTOMETRY COMPETENCIES: WHAT DO STUDENTS NEED TO KNOW?
Bibliographic record
Abstract
Objective: The purpose of this study was to define the core competenciesto include in an undergraduate optometry placement program. Methods: We selected a participatory 12-month action-research project approach to define a set of core competencies to drive the learning process during optometryplacements. Four stages were scheduled; 1) literature review(focus group to define a list of competencies); 2) assessment by university optometry staff(one wave Delphi survey); 3) assessment by external stakeholders, [final year students(n=25), optometrists in practice (n=20) and members of The College of Optometrists Board (n=9)] prior to the development of placements (Likert scale, on-line questionnaire); 4) placement development and analysis including students’ logbook reviews by the research team andstudents’ and placement supervisors’ feedback. Results: 72 core competencies classified into 8 major units was proposed after the focus group analysis (General Optical Council (UK), ASCO (EEUU); Optometry Australia and The Canadian Examiners in Optometry mapping) with high levels of consensus between university staff members (Delphi survey) and external stakeholders.Acompetencies-based logbook was created and used during student placements yielding high levels of satisfaction amongst both students and supervisors (7.6±1.2 and 7.4±1.6 over 10 respectively). Conclusions: This study demonstrates the use of a systematic method toobjectively develop undergraduate core competenciesby asking different external and internal university stakeholdersto identify competencies that are relevant inday-to-day professional practice. Similar methodology could be used in other programs, and provides rational and transparent means of developing competenciesin the education ofhealth care students.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".