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Record W2913208209 · doi:10.11575/prism/36112

An Economic Evaluation of Body Checking Policies in Bantam Ice Hockey

2019· dissertation· en· W2913208209 on OpenAlexaboutno aff
Raymond Lee

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsIce hockeyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Sport-related injury is the leading cause of injury in youth and are costly to the healthcare system. Disallowing body checking in Pee Wee (ages 11-12) ice hockey has been found to be effective in reducing the risk of injuries and associated healthcare costs, however the impact on injury risk and costs in Bantam (ages 13-14) remains unknown. The objectives of this study are to compare injury rates and costs between non-elite (lower 70% divisions of play) Bantam players in leagues allowing body checking to where body checking is disallowed, and to project the overall change on the number of injuries and costs to the Alberta healthcare system if body checking were disallowed for all Bantam players over one season. The study found that disallowing body checking reduced injuries by 4.32/1000 player-hours and saved cost by $1,737/1000 player-hours in the public healthcare system. This policy change could potentially prevent 1,102 injuries that occur during games and save $331,522 in the public healthcare system over one season in Alberta. However, this study used injury rates adjusted only for exposure hours and team clustering, but not other covariates or repeated observations. Thus further analysis is required before policy recommendations can be made.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.294
Teacher spread0.269 · 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 designQualitative
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
Published2019
Admission routes1
Has abstractyes

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