Determinants of implementing reablement into research or practice: A concept mapping study
Bibliographic record
Abstract
PURPOSE: Reablement is a health and social model of care gaining international prominence. It is included in some publicly funded healthcare systems in Australia, Canada, United Kingdom, Norway, Sweden and other European countries. To advance reablement research and practice, we aimed to synthesize expert opinion on opportunities and challenges to delivering care with this model. METHODS: We invited authors of reablement publications and other experts from the field to take part in a three-step online concept mapping exercise: (i) brainstorming statements based on a focus prompt; followed by (ii) sorting and (iii) rating statements. We invited 63 participants, of whom 19 participants generated 114 statements. Two authors reviewed each statement independently then met three times to determine one main idea/statement and removed unrelated or duplicate ideas. The research team used concept mapping software and online and email discussion to generate clusters or groups of determinants. RESULTS: There were 58 statements for sorting and rating; 11 and 12 participants completed the sorting and rating steps, respectively. The five clusters were person and caregiver elements for participation; key reablement components for success; reablement content and delivery; organizational factors; and provider beliefs and training. Statements rated as both highly important and feasible to implement into practice were generally captured under the domains of goal setting and pursuit and person-centred care. CONCLUSION: These results generate hypotheses for future research and practice in reablement for older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".