Adaptation following stroke: A personal projects analysis.
Bibliographic record
Abstract
OBJECTIVES: Assessment of adaptation following stroke has tended to focus either on acceptance of disability or global indicators of well-being. People with stroke, however, tend to view adaptation in terms of reengagement with personally valued activities. We model the adaptation process by assessing change in importance, control, stress, challenge, pleasure, support and self-identification of personal projects (i.e., one's current activities such as work, leisure, and recreational activities) from prestroke to 24 months poststroke. METHOD: Personal projects, general health, and general well-being were assessed via interviews with a sample of 67 community-residing stroke survivors (39 male; mean age = 64.7 years, SD = 13.2) on five occasions over the first 24 months poststroke. RESULTS: Multilevel (hierarchical) modeling of the longitudinal data indicates that project dimensions of Control, Stress, Challenge, Pleasure, and Support predict well-being in expected ways. Although projects at 6 months poststroke were rated as more important, stressful, challenging, and supported by others and less controllable and pleasurable than prestroke projects, by 12 to 18 months all project ratings had returned to prestroke levels, thereby suggesting successful adaptation. CONCLUSIONS/IMPLICATIONS: Longitudinal analysis of survivors' participation in valued activities poststroke revealed a pattern of adaptation that relates to but goes beyond that suggested by global measures of health, functioning, and well-being. The focus on adaptation of personal projects or valued activities may provide a helpful way of examining and improving well-being poststroke and offer new insights to inform the development of effective interventions for improving well-being following stroke. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".