Development and Evaluation of a Nurse Practitioner Huddles Toolkit for Long Term Care Homes
Bibliographic record
Abstract
Long-term care homes (LTCHs) were disproportionately affected by the coronavirus disease (COVID-19) pandemic, creating stressful circumstances for LTCH employees, residents, and their care partners. Team huddles may improve staff outcomes and enable a supportive climate. Nurse practitioners (NPs) have a multifaceted role in LTCHs, including facilitating implementation of new practices. Informed by a community-based participatory approach to research, this mixed-methods study aimed to develop and evaluate a toolkit for implementing NP-led huddles in an LTCH. The toolkit consists of two sections. Section one describes the huddles' purpose and implementation strategies. Section two contains six scripts to guide huddle discussions. Acceptability of the intervention was evaluated using a quantitative measure (Treatment Acceptability Questionnaire) and through qualitative interviews with huddle participants. Descriptive statistics and manifest content analysis were used to analyse quantitative and qualitative data. The project team rated the toolkit as acceptable. Qualitative findings provided evidence on design quality, limitations, and recommendations for future huddles.
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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 | 0.053 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".