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Record W3198602872 · doi:10.1123/iscj.2020-0072

An Exploration of the Content and Quality of Online, Text-Based Coach Development Programs Specific to Parasport

2021· article· en· W3198602872 on OpenAlexaff
Janet A. Lawson, Jennifer Turnnidge, Amy E. Latimer‐Cheung

Bibliographic record

VenueInternational Sport Coaching Journal · 2021
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsIntrapersonal communicationInterpersonal communicationThe InternetQuality (philosophy)Web resourceResource (disambiguation)Computer scienceFocus (optics)Knowledge managementEducational resourcesPsychologyWorld Wide WebPedagogy

Abstract

fetched live from OpenAlex

Nonformal learning opportunities, such as accessing text-based online resources, are an important means of developing parasport coaches’ knowledge of how to coach. However, the focus of such resources, as well as their quality and quantity, are unknown. Using an adapted version of Lefebvre, Evans, Turnnidge, Gainforth, and Côté’s taxonomy of coach development programs, the authors explored and cataloged text-based online resources for parasport coaches identified through a broad Internet search. In addition, the technical quality of these resources was evaluated. After cataloguing and evaluating 136 resources, professional knowledge domains, specifically pedagogy and planning, were identified as the most commonly targeted domains of focus. The least frequently cited professional domains of focus were maltreatment, movement fundamentals, and preventative health and health promotion. Limited resources addressed interpersonal and intrapersonal domains of focus. Resource quality varied greatly, but the overall quality was low indicating that increasing the technical quality of online resources should be prioritized in the future.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.371

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 head0.204
GPT teacher head0.408
Teacher spread0.204 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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