Stories from select Saskatchewan formal registered nurse leaders in policy : a content analysis
Bibliographic record
Abstract
Registered Nurses (RNs) have a history of policy leadership that has altered the health care system and the profession.The purpose of the qualitative inquiry was to describe the experiences of six select Saskatchewan formal Registered Nurse leaders (RNLs) in policy.Through open-ended interviews and letters, personal experiences were interpreted using content analysis.The researcher identified key ideas from the interview data and requested a reflective letter expanding or clarifying the chosen text, serving to enhance triangulation and memberchecking of personal transcripts.Meaningful patterns and/or similarities describing three themes of values, vision, and career paths emerged from the textual data.The coding framework evolved into ten categories describing individual experiences, such as mentoring, change management, and work-life balance.Three RNLs described how they wished more RNs were involved in policy, as they believed that RNs could harness more power in policy processes.Five RNLs told stories about how graduate education influenced their thinking and they gained appreciation for leading action on policy issues.The qualitative data were presented in categories for discussion.One RNL described how organizational structures may a limiting factor to RNs" participation in policy.Implications and recommendations of the findings are outlined for education, practice, administration, research, and policy.Findings are relevant for professional, health care, and government organizations, as well as education programs.Relevance may be found by individual practitioners considering a leadership role, to assist in informing potential career paths.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".