CORE UNDERGRADUATE OPTOMETRY COMPETENCIES: WHAT DO STUDENTS NEED TO KNOW?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it