Using a complex adaptive system lens to understand family caregiving experiences navigating the stroke rehabilitation system
Bibliographic record
Abstract
BACKGROUND: Family caregivers provide the stroke survivor with social support and continuity during the transition home from a rehabilitation facility. In this exploratory study we examined family caregivers' perceptions and experiences navigating the stroke rehabilitation system. The theories of continuity of care and complex adaptive systems were integrated to examine the transition from a stroke rehabilitation facility to the patient's home. This study provides an understanding of the interacting complexities at the macro and micro levels. METHODS: A convenient sample of family caregivers (n = 14) who provide care for a stroke survivor were recruited 4-12 weeks following the patient's discharge from a stroke rehabilitation facility in Ontario, Canada. Interviews were conducted with family caregivers to examine their perceptions and experiences navigating the stroke rehabilitation system. Directed and inductive content analysis and the theory of Complex Adaptive Systems were used to interpret the perceptions of family caregivers. RESULTS: Health system policies and procedures at the macro-level determined the types and timing of information being provided to caregivers, and impacted continuity of care and access to supports and services at the micro-level. Supports and services in the community, such as outpatient physiotherapy services, were limited or did not meet the specific needs of the stroke survivors or family caregivers. CONCLUSION: Relationships with health providers, informational support, and continuity in case management all influence the family caregiving experience and ultimately the quality of care for the stroke survivor, during the transition home from a rehabilitation facility.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".