Individualized Intervention Model Aiming to Permanently Change Life Habits which Have an Impact on Modifiable Risk Factors
Bibliographic record
Abstract
Background: A great challenge for clinicians working in a rehabilitation or prevention setting is to succeed in getting their clients to be physically active and to make them change their life habits. Five years ago at Centre de Réadaptation Lucie Bruneau, an interdisciplinary rehabilitation team took that challenge. This team is specialized in stroke rehabilitation and, in the last 5 yr, it has noticed a decline in the health condition, quality of life and social participation of its patients. Here are some statistics that illustrate the situation: 57% are < 49 yr old, 24% have 3 modifiable risk factors, another 24% have four or more and 18% have five; 35% have associated heart, vascular problems or diseases; for most of them, there’s a 20% chance of having another stroke. Methods: From these observations, an innovative concept of intervention was born. It is a part of the rehabilitation process and is based on research in a variety of fields: the transtheoric model of Prochaska and DiClemente, the person centered neurodynamic approach; the ecological, psycho-biological (affordance) and pedagogical approaches. The team systematically identifies the risk factors in an individualized intervention plan which is based on the “handicap production process” model. From the start to the end of the rehabilitation program and according to his needs, the patient will be brought to understand, adopt and integrate new life habits in the following spheres: food, smoking, and physical activity. Results: This global responsibilization optimizes and strengthens their long term commitment. Concerning physical activity, results obtained from a pilot study show us that 70% of our patients maintain their level three months after their discharge. Conclusion: This type of intervention could be used in other type of settings (rehabilitation or prevention): hospital, school, CLSC’s and community centers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".