The Codman Award Paper: quality of life in stroke survivors and their spouses: predictors and clinical implications for rehabilitation teams.
Bibliographic record
Abstract
Valuing and promoting quality of life after stroke is an essential component of practice for neuroscience nurses and other clinicians working in rehabilitation settings. Although some research studies have addressed factors that contribute to quality of life post-stroke, the majority of studies have focused on stroke survivors, not their spouses. Additionally, little attention has been given to family strengths associated with quality of life. In this paper, the investigator presents the findings of a recent descriptive, correlational study that was based on the conceptual framework of the Developmental Model of Health and Nursing (DMHN) (Allen & Warner, 2002; Ford-Gilboe, 2002a). This was the first study to examine the relationships among hope, family health promoting activity, and quality of life. The study was conducted with a convenience sample of 40 stroke survivors with moderate to severe functional impairments and their spouses. Participants had completed a rehabilitation program. Spouses' employment status, number of supports, and functional independence at discharge were common predictors of quality of life for both partners. However, hope was found to contribute to quality of life of stroke survivors, but not their spouses. The different patterns of findings are discussed and the key implications for clinical and research practice are addressed.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".