Self-initiated management approaches in everyday occupations used by people with acquired cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Striving to cope with day-to-day challenges is a basic human behaviour. Self-initiated management approaches provide a resource that has yet to be discovered and systematically used in occupational therapy practice. This resource might be especially important for people with dementia who are less likely to adopt management approaches initiated by others. AIMS/OBJECTIVES: Based on the findings of former studies on management and problem-solving actions among people with cognitive impairment, this article aims to identify and categorize the study participants' self-initiated management approaches and how these may be manifested in strategies in everyday occupations. MATERIAL AND METHODS: The study utilized a form of meta-synthesis; an aggregated analysis, where findings from 11 studies published 2004-2020 were compared and categorized, incorporating the views of participants with dementia/mild cognitive impairment, or acquired brain injury. RESULTS: Thirty strategies were identified and grouped into seven categories of management approaches, used in a variety of everyday occupations and situations. A majority of the strategies were found in both populations, suggesting that management approaches are more similar than different across populations with cognitive impairment. CONCLUSIONS AND SIGNIFICANCE: Attending to the rich variation of self-initiated management approaches/strategies among persons with cognitive impairment offers possibilities for developing occupational therapy interventions.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".