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Record W4388722777 · doi:10.4085/1062-6050-0658.22

Clinical Commentary: Depression and Anxiety in Adolescent and Young Adult Athletes

2023· article· en· W4388722777 on OpenAlexaff
Margot Putukian, Keith Owen Yeates

Bibliographic record

VenueJournal of Athletic Training · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAthletesAnxietyDepression (economics)Mental healthPsychiatryStressorMedicinePsychologyClinical psychologyPhysical therapy

Abstract

fetched live from OpenAlex

Mental health (MH) symptoms and disorders are common in adolescents and young adults, and athletes may be at risk due to sport-specific triggers such as injury or illness as well as stressors related to performance, transition, or retirement from sport. Anxiety and depression are reported frequently in this age group, and early recognition and treatment can improve outcomes. The medical team (eg, athletic trainers or therapists, team physicians) should be familiar with the symptoms of depression and anxiety, recognize "red flags" for these symptoms and disorders, and seek to provide screening assessments and develop MH plans and MH emergency action plans. As a part of their scope of practice, team physicians should have the initial assessment and management of patients with these MH conditions and appreciate the importance of referrals to other MH providers with expertise caring for athletes. Athletic trainers are often the first point of contact for athletes who may be experiencing MH symptoms and therefore play a key role in early recognition and referrals to team physicians for early diagnosis and treatment. Additional resources that provide more in-depth information regarding the treatment and management of anxiety and depression are provided herein.

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)0.001
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 score0.335
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.323
Teacher spread0.292 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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