How collaborative mental health care for competitive and high-performance athletes is implemented: A novel interdisciplinary case study
Bibliographic record
Abstract
Introduction: Collaborative care is considered a best practice in mental health care delivery and has recently been applied in high-performance sport to address athletes' mental health needs. However, how the collaborative process unfolds in practice in the sport setting has not yet been well documented. The purpose of this illustrative case study was to investigate a novel interdisciplinary approach used within the Canadian Centre for Mental Health and Sport (CCMHS) to provide mental health care to clients. Focusing on 'how' the approach was implemented, the aim of the study was to provide insight into the collaboration that occurred between mental performance and mental health practitioners to provide care to a high-performance athlete over an 11-month period, as well as factors facilitating and impeding the team's collaboration. The case involved three practitioners and a 16-year-old female athlete experiencing chronic pain, low mood, and elevated anxiety. Methods: In the first phase of the data collection process, each practitioner engaged in guided reflective journaling to describe the case and reflect on their practice and outcomes. During the second phase, practitioners co-created a case timeline to describe the collaborative process using clinical documents. Lastly, practitioners participated in collaborative reflection to collectively reflect more broadly on collaboration practice occurring within the CCMHS and Canadian sport system. Results: The data depict a complex care process in which the necessity and intensity of collaboration was primarily driven by the client's symptoms and needs. A content analysis showed that collaboration was facilitated by the CCMHS' secure online platform and tools, as well as individual practitioner and team characteristics. Collaboration was, however, hindered by logistical challenges, overlapping scopes of practice, and client characteristics. Discussion: Overall, there were more perceived benefits than drawbacks to providing collaborative care. While flexibility was required during the process, deliberate and systematic planning helped to ensure success. Factors such as interdependence of collaborative practice, complementarity of practice within care teams, compensation for collaboration, in-person versus virtual delivery, and intricacies of care coordination should be further examined in the future to optimize collaborative mental health care in sport.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| 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".