Whom Do I Trust to Represent Me? Long-Term Care Resident and Family Perspectives on Legitimate Representation
Bibliographic record
Abstract
Introduction: Public engagement in long-term care policy making in Canada has primarily focused on "intermediary agents" who speak on behalf of long-term care (LTC) residents and their family caregivers. Yet the legitimacy of these intermediaries, as perceived by those they represent, has gone largely unexplored. This study examines LTC resident and family perspectives on who can legitimately represent them in LTC policy making. Methodology: We used an interpretive description design, drawing on semi-structured interviews with LTC residents and family caregivers in Ontario, Canada. Data were analyzed using inductive thematic analysis. Results: Eighteen interviews were conducted with 19 participants. Three key characteristics of legitimate representatives were identified: (1) willingness to act in the best interests of residents and families, (2) having the necessary skills and capacity to participate in LTC policy making and (3) engaging directly with residents and families. Conclusion: Governments and civil society organizations seeking to establish and maintain legitimacy in the eyes of LTC residents and family members can pursue this goal by supporting intermediaries who mirror the identities or experiences of those they represent, who are dedicated to serving their interests and who routinely and directly engage with them to understand the realities of LTC.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.028 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".