Importance of Team Functioning as a Target of Quality Improvement Initiatives in Nursing Homes: A Qualitative Process Evaluation
Bibliographic record
Abstract
INTRODUCTION: Quality improvement interventions demonstrate variable degrees of effectiveness. The aim of this work was to (1) qualitatively explore whether, how, and why an academic detailing intervention could improve evidence uptake and (2) identify perceived changes that occurred to inform outcomes appropriate for quantitative evaluation. METHODS: A qualitative process evaluation was conducted involving semistructured interviews with nursing home staff. Interviews were analyzed inductively using the framework method. RESULTS: A total of 29 interviews were conducted across 13 nursing homes. Standard processes to reduce falls are well-known but not fully implemented due to a range of mostly postintentional factors that influence staff behavior. Conflicting expectations around professional roles impeded evidence uptake; physicians report a disconnection between the information they would like to receive and the information communicated; and a high proportion of casual and part-time staff creates challenges for those looking to effect change. These factors are amenable to change in the context of an active, tailored intervention such as academic detailing. This seems especially true when the entire care team is actively engaged and when the intervention can be tailored to the varied determinants of behaviors across different team members. DISCUSSION: Interventions aiming to increase evidence-based practice in the nursing home sector need to move beyond education to explicitly address team functioning and communication. Variability in team functioning requires a flexible intervention with the ability to tailor to individual- and home-level needs. Evaluations in this setting may benefit from measuring changes in team functioning as an early indicator of success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.184 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".