‘It would nearly put the life back into you’ Older adults’ experiences of a Community Specialist Team for Older People (CSTOP) service model in Ireland: A Qualitative Descriptive Study
Bibliographic record
Abstract
Abstract Introduction In Ireland, there has been a substantial recent investment in the Community Specialist Team for Older People (CST OP) service model. This approach provides timely integrated assessment and intervention for older adults in the community by a specialist multidisciplinary team. To inform the ongoing development and refinement of the CST OP service model, and ensure it is responsive to the needs and preferences of older adults, it is important to understand how older adults experience this new model of care. This qualitative descriptive study aims to resolve a research gap by exploring older adults’ experiences of the CST OP service model. Methods A qualitative descriptive study design was employed to explore older adults’ experiences of the CST OP service model. Purposive non-probability sampling was used to recruit 13 older adults who had completed intervention with a CST OP intervention. All interviews were completed in participants own homes, audio recorded and transcribed verbatim. A reflexive approach to thematic analysis guided data analysis. Findings Three themes were identified; older adults were uncertain about what to expect from the CST OP service and encountered accessibility barriers (theme1); the CST OP team provided coordinated, comprehensive care and built strong relationships with older adults (theme 2); CST OP intervention enabled older adults to better manage everyday activities and long-term conditions, thereby improving their wellbeing (theme 3). Discussion/ conclusion Our findings highlight the importance of CGA in community-based care for older adults. Further research is needed to address access barriers and evaluate older adults’ experiences with case management and care coordination in the CST OP service model.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".