Exploring the Effectiveness of Multisource Feedback and Coaching with Nurse Practitioners
Bibliographic record
Abstract
BACKGROUND: While multisource feedback and coaching have shown promise as effective professional development strategies for physicians, the effectiveness of these interventions with nurse practitioners - a growing profession in Canada - remains unknown. Despite this knowledge gap, multiple nursing colleges in Canada require their nurse practitioner members to participate in multisource feedback processes. METHODS: An exploratory study was performed with twelve nurse practitioners using an online multisource feedback process (based on the CanMEDS Framework) and an in-person coaching session (using the R2C2 Model). Participants were surveyed immediately post intervention and two months later. Perspectives of the coaches and process coordinators were also assessed. RESULTS: Nearly all participants reported that the intervention was valuable for their professional development, and 63% reported they changed an aspect of their practice because of participating. However, the majority of participants reported difficulty finding colleagues who could provide them with valid feedback. This was because of the independent nature of their practice. CONCLUSIONS: Multisource feedback and coaching show promise as effective professional development strategies for nurse practitioners who work in collaborative practices. Further research is needed to confirm these exploratory 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 | 0.023 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".