Participatory Inquiry with a Colleague: An Innovative Faculty Development Process
Bibliographic record
Abstract
Clinical evaluation is central to the aims of nursing education; however, little has been written about the actual evaluative practices of nurse educators and the sources of influence on those practices. In this article, we describe our experience as co-investigators into the evaluation practices of one of us (J.A.J.) and overview some of the challenges and opportunities of participatory inquiry. We describe how J.A.J.'s involvement in this research project encouraged her to explore her evaluative practices in a meaningful way, which moved her to a place of deeper understanding about her work and transformed her as a nurse educator. As a result of this study, we are convinced of the necessity for a faculty development process whereby colleagues commit to a critical inquiry process focused on identifying each other's evaluative practices and sources of influence on those practices. In addition, schools of nursing can encourage faculty participation in peer development by adopting an expanded view of scholarship in which the disciplined examination of one's evaluative practices is accepted as evidence of the scholarship of teaching.
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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 | 0.146 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".