A mixed methods exploration of the team and organizational factors that may predict new graduate nurse engagement in collaborative practice
Bibliographic record
Abstract
Although engagement in collaborative practice is reported to support the role transition and retention of new graduate (NG) nurses, it is not known how to promote collaborative practice among these nurses. This mixed methods study explored the team and organizational factors that may predict NG nurse engagement in collaborative practice. A total of 514 NG nurses from Ontario, Canada completed the Collaborative Practice Assessment Tool. Sixteen NG nurses participated in follow-up interviews. The team and organizational predictors of NG engagement in collaborative practice were as follows: satisfaction with the team (β = 0.278; p = 0.000), number of team strategies (β = 0.338; p = 0.000), participation in a mentorship or preceptorship experience (β = 0.137; p = 0.000), accessibility of manager (β = 0.123; p = 0.001), and accessibility and proximity of educator or professional practice leader (β = 0.126; p = 0.001 and β = 0.121; p = 0.002, respectively). Qualitative analysis revealed the team facilitators to be respect, team support and face-to-face interprofessional interactions. Organizational facilitators included supportive leadership, participation in a preceptorship or mentorship experience and time. Interventions designed to facilitate NG engagement in collaborative practice should consider these factors.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".