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Record W3217000206 · doi:10.3233/wor-205111

The educational needs of Canadian homeless shelter workers related to traumatic brain injury

2021· article· en· W3217000206 on OpenAlexaffabout
Amanda Formosa, Isabelle Dobronyi, Jane Topolovec‐Vranic

Bibliographic record

VenueWork · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsTraumatic brain injuryContext (archaeology)MedicinePsychological interventionPopulationOccupational safety and healthNursingHealth carePsychiatryFamily medicinePsychologyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Traumatic brain injury (TBI) has a higher prevalence in the homeless population. Caregivers to individuals who have TBIs may require better education surrounding screening, diagnosis and management of this disease to tailor interventions to their clients' needs. OBJECTIVE: To assess the insight and educational needs of homeless care providers in recognizing and dealing with clients who had experienced a TBI. METHODS: A survey assessing the point of views of homeless care providers across Canada regarding their level of confidence in identifying and managing symptoms of TBI. RESULTS: Eight-eight completed surveys were included. Overall, frontline workers expressed a moderate level of confidence in identifying and managing TBI, stating that educational initiatives in this context would be of high value to themselves and their clients. CONCLUSIONS: Frontline workers to homeless clients rate their educational needs on the identification and management of TBI to be high such that educational initiatives for shelter workers across Canada may be beneficial to increase their knowledge in identifying and managing the TBI-related symptoms. Improved education would not only benefit frontline workers but may also have a positive effect on health outcomes for their clients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.389
Teacher spread0.349 · 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.

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
Published2021
Admission routes2
Has abstractyes

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