How Residents’ Quality of Life are Represented in Long-Term Care Policy: A Novel Method to Support Policy Analysis
Bibliographic record
Abstract
<strong>Context:</strong> In one’s final years, quality of life (QoL) is a fundamental desire. In Canada, a publicly-funded long-term care (LTC) system is governed provincially through multiple policies about housing and care provision. A pan-Canadian research team investigated federal and provincial policies’ influence on the QoL of older people living in residential LTC in four provinces: British Columbia, Alberta, Ontario, and Nova Scotia. <strong>Objective(s):</strong> This paper describes a novel method of policy analysis developed by the authors to analyse the inclusion of QoL domains within these LTC policies, and assess implications for residents, their families, and staff. <strong>Method(s):</strong> Within the novel method mentioned there were four stages in the method that consisted of an iterative and collaborative approach to understanding the relationships between LTC regulations and resident QoL domains through four perspectives (resident, staff, family, volunteer). At first, inclusion/exclusion criteria were applied to select appropriate policies, and secondly, policy texts were to coded according to Kane’s (2001) QoL domains. The third stage involved assigning a level of regulatory power, with the final stage interrogating the policy categorisation data from four perspectives: residents, families, volunteers and workers. <strong>Findings:</strong> The outcome revealed a dominant discourse of safety, security, and order over other domains such as dignity, privacy, and spirituality. <strong>Limitations:</strong> Policies dictate regulatory and guiding principles, and are only one part of the story. How these policies are implemented is beyond the scope of our research, but we recognize that understanding these implementation practices are essential to fully capture the experiences of residents, their families, and staff. <strong>Implications:</strong> This novel method is useful in exploring how QoL is supported across a high number of complex cross-jurisdictional policies. We conclude that our approach to policy analysis enables a re-examination of policies affecting LTC and assesses whether these policies reflect the values of the residents and society at large.
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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 | 0.043 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".