Stroke Patients' Experience with the Australian Health System: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Consumer feedback about experiences with the health system is integral to service planning and is consistent with growing interest in patient-centred care. PURPOSE: To explore the experiences of community-dwelling stroke survivors at one, three and five years using a community-based, cross-sectional study. METHODS: The quantitative study was comprised of 90 participants post-stroke (3 cohorts, each including 30 participants). Qualitative interviews were undertaken with 12 participants (4 participants from each cohort). This paper presents findings from the qualitative component of the project. FINDINGS: The majority of participants in each cohort were independent and a high proportion required community services. Qualitative data identified varied experiences with the health system, including knowledge about stroke, communication with the health system, and influences on transition home. IMPLICATIONS: The results presented identify the need for ongoing health professional education to enhance stroke service delivery. There is a particular need to address stroke risk-factor modification and to ensure close collaboration with patients and other health professionals with regard to rehabilitation processes. Results identify experiences with health systems up to five years post-stroke. Occupational therapy can play an essential role in post-stroke education and in rehabilitation focused on adjustment to stroke.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".