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Record W2075097231 · doi:10.3109/02699052.2014.890744

Systems analysis of community and health services for acquired brain injury in Ontario, Canada

2014· article· en· W2075097231 on OpenAlexafffundabout
Sarah Munce, Rika Vander Laan, Charissa Levy, Daria Parsons, Susan Jaglal

Bibliographic record

VenueBrain Injury · 2014
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAccountabilityMental healthFocus groupHealth careQualitative researchAcquired brain injuryMedicineNursingRehabilitationPsychologyBusinessPsychiatryPolitical scienceMarketing

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: To conduct a systems analysis on community and health services for individuals with acquired brain injury (ABI) in the province of Ontario, Canada. RESEARCH DESIGN: This study employed a triangulation design. This design is used when there is a need to validate quantitative results with qualitative data, as is the case in the present study. METHODS AND PROCEDURES: Forty-two healthcare professionals and/or healthcare administrators from organizations across the province and across the continuum of care were surveyed. A 1-day focus group was also held to validate the study findings. MAIN OUTCOMES AND RESULTS: The main results of this study revealed: (1) a lack of services for children/adolescents; (2) service gaps for individuals with co-existing mental health conditions; (3) a lack of services related to employment; (4) changes in casemix, in terms of more individuals with co-morbid medical and mental health conditions (with many of the organizations reporting medical instability and severe behavioural disorders as exclusion criteria); and (5) a need for more organizations to track patient outcomes for evaluation and/or accountability purposes. CONCLUSIONS: Findings from this study will lead to improvement of current services but also improved planning of future services for individuals with ABI.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.223
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.357
Teacher spread0.292 · 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

Citations23
Published2014
Admission routes3
Has abstractyes

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