Enabling University Teaching for Canadian Academics with Multiple Sclerosis through Problem-Focused Coping
Bibliographic record
Abstract
BACKGROUND: Research shows that sustained employment contributes to a higher quality of life for those with multiple sclerosis (MS). Occupational therapists can work to create therapeutic interventions that assist people with MS with maintaining employment. PURPOSE: To detail the problem-focused coping strategies that academics with MS employ to enable them to teach in universities. METHODS: Semi-structured interviews were conducted with 45 Canadian academics with MS. Thematic analysis was used to generate findings. FINDINGS: While there is flexibility in research and service work tasks, teaching tasks are the most seemingly inflexible. This necessitated the development of problem-focused coping strategies to enable teaching. Three categories of strategies were employed: (1) organizational; (2) before/after teaching; and (3) during teaching. IMPLICATIONS: This brief report is intended to serve as a resource for occupational therapists and others wanting to gain a better understanding of the types of therapeutic interventions useful to those teaching in universities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".