Being more conscientious, collaborative, and confident in addressing patients' fears and anxieties: nurses' perspectives
Bibliographic record
Abstract
Susan E Beswick,1 Sandee Westell,1 Sarah Sweetman,1 Charmaine Mothersill,1 Lianne P Jeffs1,21St Michael's Hospital, Toronto, ON, Canada; 2Keenan Research Centre of the Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada Background: Developing a therapeutic partnership between patient and nurse is key to ensuring the patient's needs and preferences are identified, addressed, and valued as a key patient safety goal. There is growing recognition that patients living with chronic lung diseases often experience increased levels of stress, anxiety, and depression compared to their healthy counterparts. Creating strategies for early identification and management of patients' fears and anxieties is a strategy to minimize anxiety and depressive symptoms.Methods: This article provides an overview of a qualitative study which explored nurses' perceptions and experiences associated with the implementation of the Registered Nurses' Association of Ontario's Establishing Therapeutic Relationships Best Practice Guideline that focused on strategies to alleviate patients' fears and anxieties on one respirology unit.Results: Study findings suggest that involvement in Best Practice Guideline implementation enabled nurses to address patients' fears and anxieties in a focused, conscientious manner and to be more collaborative and confident in their care.Conclusion: Providing opportunities for nurses to learn and apply evidence-based practice around therapeutic patient-centered care is a key step in ensuring a quality patient experience.Keywords: evidence-based practice, best practice guideline, therapeutic relationship, fear and anxiety, collaborative practice
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".