“Why Do We Always Have to Focus on the Bad”: A Strengths-Based Approach to Identify the Positive Aspects of Care From the Perspective of Older Adults Using a Secondary Qualitative Analysis
Bibliographic record
Abstract
Hospitalization is often viewed as a burdensome and stressful period for older adults and their family caregivers; however, little attention has been given to the positive aspects of the care continuum journey. The purpose of this article is to highlight the positive aspects of healthcare from the perspective of Canadian older adults with complex needs and their family caregivers. This study utilized a strengths-based theoretical perspective to conduct a secondary qualitative analysis of interviews with 12 older adults and seven family caregivers. Four themes relating to positive aspects of care were identified, including: (1) looking beyond illness, (2) emotional support from healthcare providers, (3) timely discharge, and (4) upholding independence. Focusing on the positive aspects can help determine areas of care practice that currently work well. These insights will be valuable for current and future initiatives seeking to restructure and optimize healthcare services for older adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".