Factors Influencing Resident Responsive Behaviors Toward Staff in Nursing Homes: A Systematic Review
Bibliographic record
Abstract
Abstract When staff experience various types of resident responsive behaviors, this can lead to decreased quality of work-life and lower quality of care. We synthesized empirical quantitative and qualitative evidence on factors associated with resident responsive behaviors directed towards staff in nursing homes. We searched 12 bibliographic databases and "grey" literature with two key words: long-term care and responsive behaviors resulting in 7671 sources. Pairs of reviewers independently completed screening, data extraction, and risk of bias assessment. Based on extracted data, we developed a coding scheme of factors utilizing the ecological model as an organizational structure. We then applied the coding scheme to quantitative and qualitative articles and prepared narrative summaries for each factor. From 86 included studies (57 quantitative, 28 qualitative, 1 mixed methods), multiple factors emerged, such as staff training about responsive behaviors (individual level); staff approaches to care (interpersonal level); leadership, staffing resources, and physical environment (institutional level); and racism and patriarchy (societal level). Quantitative and qualitative results each provided key insights, such as qualitative results pertaining to leadership responses to reports of responsive behaviors, and quantitative findings on the impact of staff approaches to care on responsive behaviors. By synthesizing both quantitative and qualitative evidence, this review provides a comprehensive overview of factors associated with resident responsive behaviors towards staff. Our findings offer insights into promising factors for long-term care system and nursing home managers to address to strive to reduce responsive behaviors of residents toward staff in nursing homes.
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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 | 0.022 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".