One year follow-up and exploratory analysis of a patient-centered interdisciplinary care intervention for multimorbidity
Bibliographic record
Abstract
CONTEXT: Interventions for people with multimorbidity have obtained mixed results. We aimed to document the long-term effect of an intervention for people with multimorbidity. METHODS: 284 patients (18-80 years) presenting three or more chronic conditions were recruited from seven family medicine groups in the Saguenay-Lac St-Jean region, Quebec, Canada. The patient-centered intervention was based on motivational approach and self-management support. Outcomes were evaluated in a one-year pre-post study design with questionnaires that included the Health Education Questionnaire (heiQ), the Self-Efficacy for Managing Chronic Diseases, the Veteran RAND-12 Health Survey (VR-12), the EuroQoL 5-Domains questionnaire, the Kessler six item Psychological Stress Scale, and measures of smoking habit, physical activity, healthy eating and alcohol consumption. Subgroup analyses by age, number of conditions, sex, and income were also conducted. RESULTS: The heiQ domain of emotional wellbeing improved significantly. Improvement was also observed for the VR-12 and the K6. Among the health behaviours, only healthy eating was improved. Subgroup analyses in this exploratory study suggest that younger patients, those with lower number of chronic conditions or higher incomes may respond better in relation to self-management, health status and health behaviours. CONCLUSION: One year after the intervention, participants significantly improved a variety of outcomes. Subgroup analyses suggest that younger patients, those with lower number of chronic conditions or higher incomes may respond better in relation to self-management, health status and health behaviours. This suggests that future interventions should be tailored to patients' characteristics including age, sex, income and number of conditions.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".