Developing Reflective Health Care Practitioners: Learning from Experience in Dental Hygiene Education
Bibliographic record
Abstract
Maintaining competence requires health care practitioners to remain current with research and implement practice changes. Having the capacity to reflect on practice experiences is a key skill, but reflective skills need to be taught and developed. This exploratory qualitative study examined the outcomes of a dental hygiene program requirement for developing reflective practitioners. Using a purposive convenience sample, students were solicited to participate in the study and submit reflective journals at the end of two terms. Eleven of twenty-six students participated in the study, providing sixty-four reflective entries that underwent qualitative thematic analysis. Using a reflective model, we identified themes, developed codes, and negotiated among ourselves to reach consensus. Results showed approximately two-thirds of the participants reached the central range as "reflectors" and most of the remaining fell within the lower range as "non-reflectors." We concluded that dental hygiene students reached similar levels of reflection to other groups and the triggers were varied, appropriate for early learners, and divided between positive and negative cues. However, the small sample represented less than one-half of the class, yielding a potentially biased sample. Therefore, we conclude that the findings provide a departure point for further research with a more cross-cutting sample in order to substantiate reflective educational requirements and validate these findings.
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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 | 0.032 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".