Online Psychological Skills Training Programs: A Systematic Review of Program Websites
Bibliographic record
Abstract
The purpose of this paper was to identify and describe sport-based psychological skills training (PST) programs available online. An eight-step systematic review methodology for identifying and describing websites was used to search for programs delivered asynchronously through online learning modules, designed for athletes, and presented in English. Information available through a program’s home page(s) (i.e., website) was used for analysis, as individual modules were not evaluated in this study. Information from these websites was assessed for readability and quality; the DISCERN instrument was used to assess the quality of information included to describe each program. Descriptive statistics and content analyses were employed to describe various program characteristics, categorized as access and audience, delivery, and content. Overall, 18 online module-based PST programs were identified. Most information on a program’s website was rated as fairly difficult to read, and DISCERN scores were often poor. Nevertheless, programs were generally framed for athletes of all competitive levels, varied considerably in how they were delivered (e.g., number of modules, time to complete), and offered content on a variety of topics in sport psychology (e.g., imagery, attentional control). This study identifies the current state of PST programs available online and provides a descriptive account of these programs. This research advances several implications for research and practice, including the need to investigate the effectiveness of online PST programs.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".