Goal-setting practices in sport psychology: An investigation into practitioner experiences
Bibliographic record
Abstract
Investigators undertaking goal-setting research in sport have often focused on the effects of goal content, while those writing professional practice literature have suggested how practitioners could set goals with clients. Few empirical investigations have concentrated on understanding how and why sport psychology practitioners (SPPs) use goal setting or the active ingredients contributing to intervention effectiveness. By adopting a 2-stage, multiple methods approach, we aimed to identify how and why SPPs used goal setting and what contributed to their successful and unsuccessful experiences of setting goals. In Stage 1, 84 accredited/certified SPPs and 16 SPPs in training on an accreditation/certification pathway completed an online survey to identify how and why they set goals. In Stage 2, we conducted semi-structured interviews with 14 participants that explored their experiences of goal setting and elaborated on findings generated in Stage 1. Our findings illustrate that goal setting is a dynamic process, and we identified several common steps across participants. Goal setting was used to enhance psychological and performance outcomes, but the process was influenced by client, contextual, and practitioner factors. Aspects perceived to influence the effectiveness of goal setting included: the attitude of the client toward goal setting; setting appropriate goals; and reflecting, tracking, and monitoring progress. When implementing goal setting in practice, our findings suggest that SPPs can expect steps of the intervention to differ between clients. Furthermore, practitioners might consider the commitment of the client to the process and following up after the intervention as factors that could contribute to successful outcomes.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".