What matters to you? A mixed-method evaluation of goal setting and attainment within reablement from a client perspective
Bibliographic record
Abstract
BACKGROUND: Goal setting is an essential component of reablement programmes. At the same time it is also an important aspect in the evaluation of reablement from the perspective of clients. OBJECTIVES: As part of the TRANS-SENIOR project, this research aims to get an in-depth insight of goal setting and goal attainment within reablement services from the perspective of the older person. MATERIAL AND METHODS: A convergent mixed methods design was used, combining data from electronic care files, and completed Canadian Occupational Performance Measure (COPM) forms with individual interviews. RESULTS: In total, 17 clients participated. Participants' meaningful goals mainly focused on self-care, rather than leisure or productivity. This mattered most to them, since being independent in performing self-care tasks increased clients' confidence and perseverance. Regarding goal attainment, a statistically significant and clinically relevant increase in self-perceived performance and satisfaction scores were observed. CONCLUSION: Although most goals focused on self-care, it became apparent that these tasks matter to participants, especially because these often precede fundamental life goals. SIGNIFICANCE: Reablement can positively contribute to goal setting and attainment of clients and may contribute to increased independence. However, effectiveness, and subsequently long-term effects, are not yet accomplished and should be evaluated in future research.
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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 | 0.056 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".