Nurse leaders’ perceptions of the impact of their participation in a reflective practice program
Bibliographic record
Abstract
Since 2003, a university healthcare centre in Quebec (Canada) has offered its nursing leaders access to a long-term professional development program focusing on skills in Reflective Practice (RP). This program is based on teaching nursing leaders to interpret and reframe difficult, emotionally-charged situations they encounter on a regular basis, so they can improve their interpersonal interactions with their colleagues, patients, and patients’ families. This article describes the results of a qualitative study conducted in 2018 with 18 nursing leaders who participated for at least three years in the RP program. Participants were asked to describe their understanding of the RP approach, key learnings from the program, and parts of the training they found most valuable. They were also asked to define or share the program’s impact on their professional practice and leadership skills. It was found that the participants view RP as a useful tool for understanding and improving their interactions with others, and that it had led to concrete improvements in their interpersonal and leadership skills. Several of the positive changes described by participants are rarely described in other studies about the use of RP as a professional development tool in a clinical nursing setting. The results suggest that when nurse leaders have participated for several years in an RP program, they experience different benefits than front-line staff with less long-term participation.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".