Family Members' Use of Private Companions in Nursing Homes: A Mixed Methods Study
Bibliographic record
Abstract
Families who are dissatisfied with the nursing home care of their family member may supplement their care by hiring a private companion. Families who have the financial resources pay for extra care, while families who cannot afford a private companion receive the current standard of care. Anecdotal evidence suggests that private companion use has increased over time. However, there is no research that examines private companions. The goal of this mixed methods study was to provide empirical evidence about who private companions are, what they do, and why they are needed. Andersen and Newman’s Health Service Utilization Model was used to understand private companion use. This study used both survey research and grounded theory. A mailed survey was completed by 280 of 432 family members of nursing home residents in a Toronto nursing home, yielding a response rate of 65 percent. Grounded theory principles were used to conduct interviews with 10 family members to understand why private companions were hired. Almost two-thirds of nursing home residents had a private companion. Family members reported that they paid about $475 per week for private companions who provided about 40 hours of care per week. Private companions were mostly women and immigrants. Private companions performed many activities including assisting with activities of daily living, toileting, feeding, escorting to activities, and providing social support. In the survey, family members reported hiring a private companion for reasons related to families’ needs (e.g. quality of care concerns), residents’ needs (e.g. deteriorating health); and staff recommendations. The family members reiterated these reasons in the interviews. Quality of care was the overarching theme that captured the reason for private companion use, which encompassed the following themes: inadequate staffing, unmet residents’ needs, overburdened family members, and suboptimal nursing home environment. The qualitative data emphasized the importance of building relationships with nursing home residents. The predictors of private companion use in the multivariate analysis were longer duration of nursing home stay, higher resident dependency, and family members’ concerns with quality of care. This research is among the first to study private companions, and has implications for research, policy, and practice.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".