Perioperative nurses' experiences of point-of-care nursing leadership: a narrative inquiry
Bibliographic record
Abstract
Front line nurses are increasingly being encouraged to engage in point-of-care nursing leadership to positively influence the health of the persons in the healthcare team and the landscape within which care ensues. Using Connelly and Clandinin’s Narrative Inquiry, I explored how perioperative nurses experience point-of-care nursing leadership in the operating room (OR). My co-participants and I engaged in narrative interviews and Schwind’s Narrative Reflective Process. Participants’ stories were re-constructed and analyzed using Narrative Inquiry’s three levels of justification (personal, practical and social), through the theoretical lenses of Leadership Model and Person-Centred Nursing. Narrative patterns that emerge are: advocacy, relationships, and teaching and learning. The participants’ stories are re-presented using poetry. Implications for nursing and healthcare include a need to embody person-centred care to inform point-of-care leadership practices. A new concept of person-centred point-of-care leadership is developed for further exploration in research.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".