An in-depth look at mental training through a narrative lens of 2012 Canadian Paralympic athletes
Bibliographic record
Abstract
The purpose of this study was to explore - through in-depth narratives - the unique experiences of 2012 Canadian Paralympic athletes and the role mental training played leading up to, and during, the London 2012 Olympics. There has been limited research investigating the role that the psychological component plays among elite Paralympic athletes; especially within a Canadian context. Four athletes who competed for Canada at the London 2012 Paralympic Games were interviewed through the use of semi-structured, exploratory interviews. Interviews were transcribed and written-up as in-depth narratives that weaved through the athletes’ unique, experiences. The interviews were first analyzed individually for themes and then compared and contrasted across all four. The results were discussed under three major themes. The first theme explored the overall attitudes the athletes had towards mental training and sport psychology. The second theme discussed the role mental training played off the court for these particular athletes. The third theme that emerged was with regards to the implementation of mental training with a proactive versus a reactive approach. Suggestions for future research include exploring the relationship between a sport psychologist and coach and how the two can work together to effectively implement mental training. Additionally, implementing mental training with a proactive versus a reactive approach may have a differential impact on athlete performance and thus should also be investigated.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".