Factors Associated With Residents’ Responsive Behaviors Toward Staff in Long-Term Care Homes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: When staff experience responsive behaviors from residents, this can lead to decreased quality of work life and lower quality of care in long-term care homes. We synthesized research on factors associated with resident responsive behaviors directed toward care staff and characteristics of interventions to reduce the behaviors. RESEARCH DESIGN AND METHODS: We conducted a mixed-methods systematic review with quantitative and qualitative research. We searched 12 bibliographic databases and "gray" literature, using 2 keywords (long-term care, responsive behaviors) and their synonyms. Pairs of reviewers independently completed screening, data extraction, and risk of bias assessment. We developed a coding scheme using the ecological model as an organizing structure and prepared narrative summaries for each factor. RESULTS: From 86 included studies (57 quantitative, 28 qualitative, 1 mixed methods), multiple factors emerged, such as staff training background (individual level), staff approaches to care (interpersonal level), leadership and staffing resources (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 behaviors, and quantitative findings on the impact of staff approaches to care on behaviors. Effects of interventions (n = 14) to reduce the behaviors were inconclusive. DISCUSSION AND IMPLICATIONS: We identified the need for an enhanced understanding of the interrelationships among factors associated with resident responsive behaviors toward staff and processes leading to the behaviors. To address these gaps and to inform theory-based effective interventions for preventing or mitigating responsive behaviors, we suggest intervention studies with systematic process evaluations.
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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 | 0.025 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| 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".