Development of an item pool for a patient reported outcome measure of resilience for people living with dementia
Bibliographic record
Abstract
Abstract Background and objectives Policies to support people living with dementia increasingly focus on strengths-based approaches, highlighting the importance of building resilience. This research responds to the lack of a suitable resilience measure for people with dementia. It develops a pool of items to inform a new measure of resilience for this population. Methods A conceptual model and associated data informed the item generation of the draft resilience measure. Regular meetings with professionals (n = 7) discussed response-scale formatting, content and face validity, leading to refinement and item reduction. Cognitive interviews with people living with dementia (n = 11) then examined the face and content validity of items and the suitability of response-scale formatting. These two phases informed subsequent revision and further item reduction of the resilience measure. Results The first item generation exercise led to 140 items. These were independently assessed by the professionals and this refinement reduced the measure to 63 items across 7 domains of the conceptual model (psychological strengths; practical approaches for adapting to life with dementia; continuing with hobbies, interests and activities; strong relationships with family and friends; peer support and education; participating in community activities; the role of professional support services). Cognitive interviews explored the 63 items with people living with dementia. Detailed feedback led to items removed due to difficulty with (a) understanding (N = 7); (b) answering (n = 11); (c) low preference for that item (n = 6); and (d) presence of a preferred item within a cluster of similar questions (n = 4). Items were amended to enhance clarity/conciseness (n = 19) leading to a final 37-item pool. Conclusion Established methods for measurement development included the expertise of people with dementia and led to the generation of a set of items for a new resilience measure that were understandable and acceptable to this target population. This 37-item pool reflects the conceptual understanding of resilience in dementia as being derived across individual, community and societal level resources.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".