Exploring Perspectives on Stroke Standard Set Data Collection: A Qualitative Descriptive Study
Bibliographic record
Abstract
ABSTRACT: BACKGROUND: The International Consortium on Health Outcome Measurement developed a standard set for stroke (SSS) that includes patient-reported outcome measures to help capture patients' perspectives on their poststroke recovery. The objective of this study was to explore the experiences and perspectives of individuals who collected SSS data from patients who were admitted to hospital for a stroke. METHODS: A qualitative descriptive approach was taken. Semistructured, audio-recorded interviews were conducted with individuals employed at 2 acute neurological inpatient units in Southwestern Ontario, Canada. Interviews were transcribed verbatim and written text responses were analyzed directly. Transcripts were coded line by line and then organized into 5 overarching themes: adoption, acceptance, appropriateness, feasibility, and sustainability. RESULTS: Six interviews were conducted with participants from varying roles (eg, nurses, manager, web developer, social worker, medical clerk). Participants reported that patients were receptive to completing the SSS. Follow-up phone calls provided a significant opportunity to monitor patients' recovery postdischarge. Many patients requested medical guidance and help navigating health and social resources for unmet stroke-related needs. Barriers to consistent SSS assessment included high employee turnover and lack of time, space, or capacity for follow-up. To sustain data collection, a dedicated, financially supported neurological nursing role was suggested. CONCLUSION: Participants were supportive of SSS data collection that could provide monitoring, oversight, and follow-up of stroke patients after discharge from acute care. However, the utility of the dataset is heavily dependent on having the data collection process properly resourced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".