Psychosocial care in DHB-based stroke services in Aotearoa: a survey of current practice
Bibliographic record
Abstract
AIM: Stroke has significant psychosocial impacts which contribute to burden for the person with stroke and affect stroke outcomes. The Psychosocial Working Group of the National Stroke Network (NSN) sought to survey current practices for assessing and supporting psychosocial needs within district health board (DHB) based stroke services to inform national service delivery initiatives. METHODS: The survey was conducted in 2021. It was distributed to senior clinicians in all DHBs via the NSN. RESULTS: Thirty-seven responses were received from stroke services, representing 90% of DHBs. Sixty-three percent of services reported some process for screening for psychosocial needs. Of these, only 11% used validated screens. Variability in the type of psychosocial support was evident. Seven percent of services had routine access to psychology, while 53% could access psychology on referral. There was limited evidence of specific screening and support processes for Māori, Pacific peoples, or those with communication impairments. Respondents identified training and resources needs to enable better psychosocial care. CONCLUSION: Stroke services are not consistently meeting national guidelines which require all services have a process for screening for psychosocial needs. This survey has informed a work programme to support psychosocial care practices in stroke services in Aotearoa New Zealand.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".