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Record W3210644836 · doi:10.1123/japa.2021-0141

Mental Performance Consultants’ Perspectives on Content and Delivery of Sport Psychology Services to Masters Athletes

2021· article· en· W3210644836 on OpenAlexaffabout
Tyler Makepeace, Bradley W. Young

Bibliographic record

VenueJournal of Aging and Physical Activity · 2021
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThematic analysisAthletesGratitudePsychologySport psychologyMental healthApplied psychologyContent analysisMedical educationSocial psychologyQualitative researchMedicinePsychotherapistSociology

Abstract

fetched live from OpenAlex

In the absence of sport psychology resources for Masters Athletes, mental performance consultants could benefit from information to assist consultancy with older adult athletes. We conducted semistructured interviews to explore 10 Canadian professional mental performance consultants' (two men and eight women) perspectives of targeted content and the nature of service delivery to Masters Athletes. Following inductive thematic analysis, results for Content of Sport Psychology related to performance readiness (e.g., preparatory routines, mental focus plans); prioritizing sport (e.g., balance/time management, recruiting social support); preserving sport enjoyment (e.g., self-reflection, gratitude/sport as opportunity); and age-related considerations (e.g., managing changing physical realities). Results pertaining to Addressing and Delivering Sport Psychology Services included considerations toward age-related attributes (e.g., values/identity, engaged/invested clients) and accommodating barriers/constraints (e.g., time, stigma). Our results show there are novel considerations when consulting with Masters Athletes, and we discuss what these findings mean for adult-oriented approaches in applied practice.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.325
Teacher spread0.289 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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