Benefits and Limitations of an Employer-Led, Structured Logbook to Promote Self-Directed Learning in the Clinical Workplace
Bibliographic record
Abstract
A structured logbook, consisting of a competency log and a learning contract, was designed and implemented as part of a two-week structured work placement for final-year veterinary students to help them become more self-directed in the workplace. The competency log encompassed 48 core skills and, along with the learning contract, was reviewed at the start and end of the placement. To assess their perceptions of the logbook in promoting self-directed learning, students and supervisors were asked to complete a questionnaire pre- and post-placement and to participate in focus groups (students) and interviews (supervisors) after the placement. The study found significant increases pre- to post-placement in students' perceived levels of competence in all 48 skills and their confidence in being self-directed. However, student attitudes toward the logbook significantly decreased in terms of it encouraging supervisors to take a clearly designed role in structuring learning and facilitating alignment of employer and student expectations. Although supervisors were generally positive about the logbook, some had not been able to review it with their students, which affected students' perceptions of the logbook's usefulness. Some supervisors felt they had not received enough training, and most, erroneously, believed the logbook to be an external research initiative rather than having been designed by the head of their own organization. This study demonstrated that a structured logbook may be useful in helping students become more self-directed; however, supervisor support for the logbook is critical. To facilitate this, supervisors require training and support from senior management.
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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 | 0.007 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".