Who Wants to be involved? Decision-Making Preferences among Residents of Long-Term Care Facilities
Bibliographic record
Abstract
While the benefits of participating in care or medical decision making are widely reported, research on decision-making participation preferences usually reveals some portion of individuals who do not want to be involved. Data collected through structured, in-person interviews with 100 residents of six long-term care (LTC) facilities in Victoria, British Columbia, were used to examine participation preferences with respect to four types of care decisions (bedtimes, medication choice, room transfer, and advance directives), as well as predictors of these preferences. Residents with higher levels of formal education, a greater number of chronic conditions, and greater confidence about the worth of their input tend to prefer more active involvement in decision making. This research also suggests that predictors of preference for independent control over decision making (active involvement) differ from predictors of preference for joint or shared decision making. Implications for the empowerment of LTC facility residents and the meaning of decision-making involvement in these environments are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".