NURSE PRACTITIONER–LED IMPLEMENTATION OF HUDDLES TO SUPPORT STAFF IN LONG-TERM CARE HOMES
Bibliographic record
Abstract
Abstract Staff working in long-term care (LTC) homes frequently report experiencing moral distress related to lack of autonomy and not being able to provide quality care. Huddles have been used as a communication tool for many years in acute care settings to improve collaboration and safety culture. In LTC homes, huddles are implemented less often, despite evidence of their benefits in improving support and teamwork. In this pre-test post-test implementation study, huddles led by a nurse practitioner (NP) were introduced in a privately-owned not-for-profit LTC home with < 150 beds, located in a medium urban centre in Ontario, Canada. Objectives of the study were to 1) examine fidelity of huddle implementation; 2) examine the extent to which the huddles improved staff’ outcomes of moral distress, job satisfaction, and support provided by the NP estimated with Bayesian proportional odds model. A total of 48 huddles were carried out by the NP over 15 weeks. Huddles were most commonly attended by personal support workers (98%) and registered practical nurses (96%), with an average of 7 individuals per huddle. Topics most often addressed at huddles were related to resident care (46%) and staff concerns (34%). Strong statistical evidence of a reduction in overall moral distress was evident for staff attending the huddles, when compared to staff who did not (posterior probability =.9933). No changes in job satisfaction and support provided by the NP were observed. Introducing huddles in LTC homes may be effective at reducing moral distress experienced by staff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".