Assessing outcomes through congruence of course objectives and reflective work
Bibliographic record
Abstract
INTRODUCTION: Course outcomes have been assessed by examining the congruence between statements of commitment to change (CTCs) and course objectives. Other forms of postcourse reflective exercises (for example, impact and unmet-needs statements) have not been examined for congruence with course objectives or their utility in assessing course outcomes. This study assessed the congruence of course objectives and statements of commitment to change, effects on practice, unmet-needs, and the utility of supplementing CTCs with other forms of reflective work in course evaluations. METHODS: A 3-module course on Alzheimer's disease and other dementias provided end-of-course CTC statements, follow-up data, and statements of effects on practice and unmet needs. Statements were aligned to module objectives and analyzed descriptively. RESULTS: Of the 932 physicians who registered for 1 of the 3 modules, 404 provided CTCs, 302 provided impact statements, and 265 provided unmet-needs statements. Sixty percent of the CTCs could be assigned to an objective for their module, and between 14% and 25% of CTCs were assigned to objectives for another module. Three-quarters of CTCs were fully or partially implemented. Physicians did not have an opportunity to implement the new content in 70% of nonimplemented CTCs. Fewer impact and unmet-needs statements were congruent with course objectives than CTCs. CONCLUSIONS: Commitment-to-change statements had more congruence with objectives than did impact or unmet-needs statements. These latter statements, particularly those that could not be assigned to an objective, may reinforce and supplement the information provided by CTC analyses.
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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 | 0.051 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".