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Record W4312559208 · doi:10.1123/tsp.2022-0067

“My Life Sucks Right Now”: Student-Athletes’ Pandemic-Related Experiences With Screen Time and Mental Health

2022· article· en· W4312559208 on OpenAlexaffabout
Martin Camiré, Camille Sabourin, Eden Gladstone Martin, Laura Martin, Nicolas Lowe

Bibliographic record

VenueThe Sport Psychologist · 2022
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAthletesMental healthPandemicThematic analysisPsychologyPsychosocialCoronavirus disease 2019 (COVID-19)ReflexivityMedical educationQualitative researchMedicinePsychiatrySociologyPhysical therapy

Abstract

fetched live from OpenAlex

The COVID-19 pandemic, and associated stay-at-home orders, instigated far-reaching disturbances in the lives of student-athletes, which included school closures and sport cancellations. The purpose of the study was to examine first-hand student-athletes’ pandemic-related experiences with screen time and mental health. A total of 22 Canadian high school student-athletes were individually interviewed in 2021. Interviews occurred online via videoconferencing and were subjected to a reflexive thematic analysis, which led to the creation of three themes: (a) pandemic life is a major grind, (b) screen time during COVID times: I feel guilty, but what else can I do? and (c) mental health during COVID times: mostly pain, but there is a silver lining. Results are discussed in terms of their implications for research and practice as it pertains to formulating endemic initiatives best supporting the many student-athletes confronting the psychosocial aftereffects of having lived through a global pandemic.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.001

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 head0.018
GPT teacher head0.330
Teacher spread0.312 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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