Optimizing Academic-Practice Partnerships to Promote Transition to Nursing Practice
Bibliographic record
Abstract
BackgroundAn academic-practice partnership was implemented in Northwestern Ontario with the goals of enhancing cross- sector collaboration, co-creating research knowledge related to transition to practice, engaging and recruiting nurses, and mobilizing knowledge to improve the transition experience. There is a growing nursing shortage requiring novel solutions to support retention, particularly for rural and remote populations. Academic-practice partnerships can be leveraged to improve working conditions and consequently job satisfaction (Padilla & Kreider, 2020; Rogers et al., 2020).MethodUsing qualitative methodology, semi-structured virtual interviews were conducted with nine Registered Nurse participants ranging in experience from three to seven months employed at the same hospital in Northwestern Ontario.The interview guide was developed collaboratively by an advisory board comprised of the researcher, hospital staff and input from two student ambassadors from the graduating class. Thematic analysis was completed and broad categories were established with data then expanded into five overarching themes.ResultsFive themes representing impactful sentiments shared by the new graduate nurses were identified: "I couldn't be the nurse I know I could be"; "I'm with you right now"; "You have to catch up"; "Do you want to learn it with me?"; and "I feel thrown in and unprepared".ConclusionNew graduate nurses experience a dissonance between expectations and reality influenced by their interactions with preceptors and colleagues. Academic-practice partnerships can create supportive learning environments, allowing new nurses to transition to independent practitioners while establishing stronger professional identity, which is a positive indicator for retention.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".