Opportunities in Multiple Sclerosis Care Partner Research: An Interview
Bibliographic record
Abstract
Guest editor Marcia Finlayson, PhD, OT Reg (Ont), OTR, is a professor in the School of Rehabilitation Therapy at Queen's University in Ontario, Canada. She began her career as a clinical occupational therapist and shifted to a research career focused on generating and sharing knowledge to help people affected by multiple sclerosis (MS) lead healthy, meaningful lives with control over their participation in daily activities, at home and in the community, particularly as they age. For this special issue on caregiving in MS, she chose to interview Kenneth Pakenham, PhD, emeritus professor of clinical and health psychology at the University of Queensland in Brisbane, Australia. For more than 4 decades, he has investigated the psychological well-being welle-eing of caregivers, including coping mechanisms and innovative interventions to improve their quality of life. His work is dedicated to applying positive health frameworks to chronic illnesses and to empowering caregivers and individuals with MS. Together, their expertise illuminates the multifaceted challenges and opportunities in MS caregiving research and understanding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".