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Record W4376224615 · doi:10.26635/6965.6096

Psychosocial care in DHB-based stroke services in Aotearoa: a survey of current practice

2023· article· en· W4376224615 on OpenAlexaff
Felicity Bright, John M. Davison

Bibliographic record

VenueNew Zealand Medical Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsOntario Stroke Network
Fundersnot available
KeywordsPsychosocialAotearoaReferralMedicineNursingNeeds assessmentStroke (engine)Family medicinePsychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.372
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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