Evaluation of the Health Promotion Model to Predict Physical Activity in Iranian Adolescent Boys
Bibliographic record
Abstract
Promoting sustainable physical activity (PA) behavior change is challenging, and a number of theoretical models have been developed and applied to this problem. Pender's health promotion model (HPM) is a relatively new model that is based on Bandura's social cognitive theory but includes the additional construct of competing demands, which are viewed as alternative behaviors (e.g., watching television) that have powerful reinforcing properties. This study evaluates the HPM as a means to predict PA in a sample of Iranian adolescent boys. Participants were 515 boys from 100 junior high and high schools in Sanandaj, Iran. Participants' mean age was 14.33 years (SD = 1.6, range = 12-17). Participants completed questions assessing social cognitive variables, and structural equation modeling was used to fit the data to the HPM. The HPM accounted for 37% of the variance in PA but did not represent a good data fit (chi(2) = 913.85, df = 473, p < .001). There were significant pathways between PA and self-efficacy (beta = .25, p < .001), enjoyment (beta = .22, p < .01), and PA modeling (beta = -.13, p < .05). A revised model that included the indirect effects of competing demands explained 34% of the variance in PA and represented a good data fit (chi( 2) = 9.12, df = 4, p = .058). In the revised model, self-efficacy, commitment to planning, and enjoyment were associated with PA. According to the HPM, competing demands influence PA. In the study sample, competing demands were not related to PA but were inversely associated with commitment to planning.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".