The components of action planning and their associations with behavior and health outcomes
Bibliographic record
Abstract
BACKGROUND: Based on the works of Kiesler and Bandura, action plans have become important tools in patient self-management programs. One such program, shown effective in randomized trials, is the Internet Chronic Disease Self-Management Program. An implementation of this program, Healthy Living Canada, included detailed information on action plans and health-related outcome measures. METHODS: Action plans were coded by type, and associations between action plans, confidence in completion and completion were examined. Numbers of Action Plans attempted and competed and completion rates were calculated for participants and compared to six-month changes in outcomes using regression models. RESULTS: Five of seven outcome measures significantly improved at six-months. A total of 1136 action plans were posted by 254 participants in 12 workshops (mean 3.9 out of 5 possible); 59% of action plans involved exercise, 16% food, and 14% role management. Confidence of completion was associated with completion. Action plan completion measures were associated with improvements in activity limitation, aerobic exercise, and self-efficacy. Baseline self-efficacy was associated with at least partial completion of action plans. DISCUSSION: Action planning appears to be an important component of self-management interventions, with successful completion associated with improved health and self-efficacy outcomes.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".