Is it worth it?: The experiences of persons with multiple sclerosis as they access health care to manage their condition
Bibliographic record
Abstract
BACKGROUND: People with multiple sclerosis (MS) require complex care throughout life. Canadians with MS are high users of health-care services, yet still report unmet health-care needs and low satisfaction with services received. OBJECTIVE: This study aimed to investigate the health-care access experiences of Ontarians with MS as they manage their condition. DESIGN AND PARTICIPANTS: Interpretive description guided data collection and analysis. Forty-eight people living across seven communities participated. Thirty-eight participated in one of five focus groups; the remaining 10 participated in an individual semi-structured interview. RESULTS: Participants described the experience of accessing care as a decisional process, guided by a form of cost-benefit analysis. The process determined whether seeking conventional health-care services 'is worth it'. Most participants felt that the energy and resources required to access the health-care system outweighed their expected outcomes, based on past experiences. Participants who did not see the benefit of care seeking turned to self-treatment, use of complementary and alternative services, and engaged in patterns of health-care avoidance until a crisis arose. DISCUSSION AND CONCLUSION: Findings suggest that a renewed effort to promote patient-centred care and a biopsychosocial approach may improve the health-care access experiences of persons with MS and reduce service avoidance.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".