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Record W2800848841 · doi:10.1111/phn.12407

Exploring concussion awareness in hockey with a First Nations community in Canada

2018· article· en· W2800848841 on OpenAlexaffabout
Cindy Hunt, Alicja Michalak, Chrissy Lefkimmiatis, Elaine Johnston, Leila Macumber, Tony Jocko, Donna Ouchterlony

Bibliographic record

VenuePublic Health Nursing · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsAboriginal Affairs Northern Dev CanadaAssembly of First NationsSt. Michael's Hospital
Fundersnot available
KeywordsConcussionIndigenousPsychological interventionPoison controlIntervention (counseling)Suicide preventionMedicineInjury preventionPhysical therapyHuman factors and ergonomicsPsychologyOccupational safety and healthFamily medicineMedical educationNursingMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this pilot study was twofold: (1) to begin to understand concussion in youth hockey in a First Nations community in Canada and (2) to determine the impact of a novel concussion education workshop. DESIGN: A one-group quasiexperimental time series study was undertaken. SAMPLE: A total of 41 participants consented, with 71% (n = 29) completing data collection at all three study time points. MEASUREMENT AND INTERVENTIONS: Two nurses one from the First Nations community and one from the tertiary care center collaborated to develop and deliver the intervention on concussion specifically general, hockey and symptom knowledge. The primary outcome was Total Knowledge Score (TKS), whereby correct responses to a self-reported questionnaire were summed and then converted to a percentage. RESULTS: The TKS were similar across study time points; preworkshop 71.7%, postworkshop 71.8%, and 6-month follow-up 72%. CONCLUSIONS: Nurses worked collaboratively with cultural experts from a First Nations community to integrate Indigenous ways of knowing into concussion awareness and safety for First Nations youth playing hockey.

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)0.001
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 score0.220
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.404
GPT teacher head0.419
Teacher spread0.015 · 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

Citations11
Published2018
Admission routes2
Has abstractyes

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