Understanding the Transition to Long-Term Care Homes: Perceptions of Family Care Partners of Older Adult Veterans
Bibliographic record
Abstract
Veterans and their family care partners represent a distinct demographic with unique needs that may differ from the general population. The transition to a long-term care home (LTCH) has the potential to profoundly impact older adult Veterans and their family care partners; however, there is a paucity of research on how this move affects them specifically. This qualitative descriptive (QD) study explored the experiences and needs of older adult Veterans and their family care partners transitioning into a LTCH from the perspective of nine care partners. Purposive sampling was used to recruit family care partners of Veterans living in LTCHs across Canada. One-time semi-structured interviews were conducted. Coding and thematic analysis were used to iteratively categorize and synthesize the data. The findings revealed three overarching themes with sub-themes: 1) information gaps and knowledge use, 2) feeling valued, recognized, and supported, and 3) the health and well-being of the Veteran/care partner. These themes were seen throughout all stages of the move to a LTCH, including the pre-transition, during-transition, and post-transition stages, and either served to impede or assist in a positive transition. The results of this study contribute to an increased awareness of the specific challenges and facilitators faced by care partners and Veterans moving into a LTCH. Further research is warranted in order to create tailored programs and policies that better support the needs of this population throughout this transition.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".