Raise Your Voice: How to Increase the Effectiveness of Resident and Family Councils in Long-Term Care Homes
Bibliographic record
Abstract
The devastating impacts of the COVID-19 pandemic highlighted the missing voices of families and residents in long-term care (LTC) decision-making and policy processes. Family and resident councils constitute one method of raising these voices, but there is currently a gap in evidence of how to promote the effectiveness of these councils. We conducted five focus groups and two interviews with LTC home leaders, residents, family members, and advocates in British Columbia using a participatory approach integrating knowledge-users throughout the research process. Using a framework analysis, we found modifiable (communication, structure, recruitment/engagement, council leadership, culture/attitudes, and resources/supports) and non-modifiable factors (medical complexity of residents and short lengths of stay) affecting council effectiveness. We discuss strategies implemented by knowledge-users to address modifiable effectiveness factors and construct a preliminary tool (a 35-question survey) that operationalizes and identifies areas that can increase council effectiveness in practice to ensure that their voices are heard in LTC decision making.
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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 | 0.082 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".