Testing two principles of the Health Action Process Approach in individuals with type 2 diabetes.
Bibliographic record
Abstract
OBJECTIVE: The Health Action Process Approach (HAPA) proposes principles that can be translated into testable hypotheses. This is one of the first studies to have explicitly tested HAPA's first 2 principles, which are (1) health behavior change process can be subdivided into motivation and volition, and (2) volition can be grouped into intentional and action stages. The 3 stage groups are labeled preintenders, intenders, and actors. METHOD: The hypotheses of the HAPA model were investigated in a sample of 1,193 individuals with Type 2 diabetes. Study participants completed a questionnaire assessing the HAPA variables. The hypotheses were evaluated by examining mean differences of test variables and by the use of multigroup structural equation modeling (MSEM). RESULTS: Findings support the HAPA's 2 principles and 3 distinct stages. The 3 HAPA stages were significantly different in several stage-specific variables, and discontinuity patterns were found in terms of nonlinear trends across means. In terms of predicting goals, action planning, and behavior, differences transpired between the 2 motivational stages (preintenders and intenders), and between the 2 volitional stages (intenders and actors). CONCLUSIONS: Results indicate implications for supporting behavior change processes, depending on in which stage a person is at: All individuals should be helped to increase self-efficacy. Preintenders and intenders require interventions targeting outcome expectancies. Actors benefit from an improvement in action planning to maintain and increase their previous behavior. Overall, the first 2 principles of the HAPA were supported and some evidence for the other principles was found. Future research should experimentally test these conclusions.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".