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Record W4304165694 · doi:10.3389/fspor.2022.945073

Monitoring mental distress in Para athletes in preparation, during and after the Beijing Paralympic Games 2022: A 22 week prospective mixed-method study

2022· article· en· W4304165694 on OpenAlexaff
Marte Bentzen, Göran Kenttä, Tommy Karls, Kristina Fagher

Bibliographic record

VenueFrontiers in Sports and Active Living · 2022
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAthletesAnxietyMental healthDistressElite athletesPsychologyMental distressClinical psychologyDepression (economics)PsychiatryMedicinePhysical therapy

Abstract

fetched live from OpenAlex

It is common in elite sport to monitor athletes' training load, injuries and illnesses, but mental distress is rarely included. An improved understanding of the epidemiology of mental distress among elite Para athletes and how their coaches perceive such monitoring would allow us to better develop and implement preventive measures. The purpose of this study was therefore to (1) prospectively describe elite Para athletes' mental distress, before, during and after the Beijing Paralympic Games (Paralympics Games 22 = PG22); and to (2) gain a better understanding of if and potentially how awareness of athletes' mental distress changed, through weekly monitoring, and influenced how coachers perceive athletes' mental distress and if they accounted for this before, during and after PG22. A mixed-method study design was used, in which prospective mental distress (depression and anxiety) data were collected weekly from 13 [Swedish] elite Para athletes in preparation, during and after PG22. Data were screened and evaluated weekly by a physiotherapist and a sports psychologist, and coaches also received weekly reports. A focus-group interview with the coaches were conducted post Paralympics to address coaches' awareness about mental distress and athlete health monitoring in Parasport. For data analyses, descriptive statistics was used for the quantitative data and a content analysis was conducted for the qualitative data. The results reveled the following proportion of datapoints indicating symptoms of anxiety and depression: before PG22 (15.8 and 19.1%); during PG22 (47.6 and 38.2%); and after PG22 (0 and 11.8%). The qualitative results indicated that coaches perceived athlete health monitoring as helpful for increasing their awareness of mental distress, and as a useful tool to initiate support for their athletes as well as improving their coaching. In summary, this cohort of elite Para athletes reported a high proportion of mental distress during the Winter Paralympic Games in Beijing. The results also show that it is important and feasible to monitor Para athletes' mental distress to detect and manage early symptoms of mental distress.

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 score0.060
Threshold uncertainty score0.572

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 head0.005
GPT teacher head0.271
Teacher spread0.266 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

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