MétaCan
Menu
Back to cohort
Record W2404392303 · doi:10.1123/iscj.2015-0110

Profiling the Canadian High School Teacher-Coach: A National Survey

2016· article· en· W2404392303 on OpenAlexaffabout
Martin Camiré, Meredith Rocchi, Kelsey Kendellen

Bibliographic record

VenueInternational Sport Coaching Journal · 2016
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoachingScholarshipPsychologyProfiling (computer programming)Medical educationWork (physics)PedagogyPolitical scienceMedicineEngineering

Abstract

fetched live from OpenAlex

Each academic year, a large number of teachers voluntarily assume coaching positions in Canadian high schools and thus undertake the dual role of teacher-coach. To date, much of the scholarship on teacher-coaches has been conducted with small samples of participants and as such, the conclusions that can be drawn about the status of the Canadian teacher-coach are limited. The purpose of the current study was to profile the Canadian high school teacher-coach using a national sample. A total of 3062 teacher-coaches (males = 2046, 67%) emanating from all Canadian provinces and territories completed a questionnaire examining personal background and work conditions. Results indicated that aspects of teacher-coaches’ personal background significantly influenced the benefits and challenges they perceived from coaching as well as the recommendations they suggested to improve their coaching experience. The recommendations put forth by the teacher-coaches to improve their work conditions must be earnestly considered by school administrators to ensure the long-term viability of the Canadian high school sport system, which is largely sustained by dedicated volunteers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.981
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.051
GPT teacher head0.349
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueInternational Sport Coaching JournalSame topicSport Psychology and PerformanceFrench-language works237,207