HOW DO FAMILY MEMBERS DEAL WITH CONFLICT IN LONG-TERM CARE? APPLICATION OF CONFLICT THEORY
Bibliographic record
Abstract
Conflict between families and staff in long term care (LTC) is a daily reality that has adverse outcomes for residents, staff and families. However, to date it has not been empirically evaluated. Multiple barriers exist in examining conflict, including its sensitive nature, which may have precluded such study, as well as lack of theoretical integration. In order to examine family-staff conflict and its management in LTC, this study has merged two independent bodies of literature, that of family caregiving in LTC and organizational behaviour literature on conflict. This study presents the argument that two prominent theories from the conflict literature, namely the theory of cooperation and competition (Deutsch, 1973) and the dual concern theory (Pruitt & Rubin, 1986) can be applied in LTC. This mixed-methods study examined family-staff conflict and conflict management in a sample of 107 family caregivers, with data showing preliminary support for the model. Results indicate that family caregivers engage in a variety of conflict resolution strategies to manage family-staff conflicts and indicate a significant role for trust, power and communication between family and staff on the frequency of conflict as well as use of cooperative and competitive conflict management. The implications of the conflict resolution strategies endorsed by family caregivers on key caregiver outcomes (i.e., family satisfaction with care and caregiver burden), theoretical fit, and evidence-based strategies for effective intervention in family-staff conflicts will be discussed.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".