An online feasibility pilot study implementing Emotion Focused Therapy for competitive athletes with moderate to severe depression and anxiety symptoms
Bibliographic record
Abstract
There has been increasing attention to mental health among athletes, yet there exist few studies evaluating clinical interventions for addressing mental disorders among athletes. The purpose of this study was to explore the feasibility of an online Emotion Focused Therapy (EFT) intervention for symptoms of anxiety and depression among competitive athletes using a pilot randomised control trial design. Sixty-nine varsity athletes were recruited; following screening, 25 (10 men, 15 women) were assigned to a treatment (n = 13) or waitlist control group (n = 12). The treatment consisted of 12 online one-hour sessions of EFT. Athletes completed measures of depression, anxiety, emotion dysregulation, alexithymia, and sport-related outcomes at baseline, post-intervention, and one-month follow-up. Athletes also completed a measure of therapist working alliance during the intervention, and follow-up qualitative interviews regarding their experiences during the intervention. Inferential statistics were not conducted; however, depression, anxiety, and emotion dysregulation scores appeared to improve for a majority of the athletes who completed the intervention, while scores on these outcomes remained stable among athletes in the control group. Analysis of the interview data indicated that the athletes felt the intervention was acceptable, and suggestions were provided regarding modification of the length and timing of the intervention. The results indicate promise for the implementation of EFT for treating anxiety and depression symptoms and for reducing emotion dysregulation among competitive athletes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".