A Model for Cultivating Dental Hygiene Faculty Development Within a Community of Practice
Bibliographic record
Abstract
There is a need to explore approaches in faculty development that will foster change in actual teaching practices. The literature suggests that there should be more deliberate use of theory in faculty development research. This study addressed this gap in the literature by exploring social learning theory in the context of communities of practice and applying this theory to a dental hygiene faculty development program. The purpose of the study was to determine if participation in a community of practice helped dental hygiene clinical instructors implement new teaching strategies by providing ongoing support for their learning. In addition, the study explored whether the level of participation in the community changed over time. A retrospective self-assessment questionnaire consisting of four open-ended questions was administered to a group of clinical dental hygiene instructors at the end of the 2010 academic year. The narrative data were analyzed thematically using qualitative methodology. The results indicated that participation in the community of practice helped clinical instructors make effective changes in their teaching practices by optimizing social learning opportunities. The responses also revealed that instructors became more comfortable participating in discussions as they identified with other members of this unique community.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".