Predicting physical activity among cancer survivors: Meta-analytic path modeling of longitudinal studies.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: We conducted meta-analyses and meta-analytic structural equation modeling of longitudinal studies among cancer survivors to (a) quantify associations between psychosocial predictors and physical activity, (b) test how psychosocial predictors combine to influence physical activity, and (c) identify study, demographic, and clinical characteristics that moderate associations. METHOD: Eligible studies used a longitudinal, observational design, included a sample of cancer survivors, and measured both a psychosocial predictor at baseline and physical activity at a later time-point. Of 2,431 records located through computerized searches, 25 independent tests (N = 5,897) met the inclusion criteria for the review. Random effects meta-analyses and meta-analytic structural equation modeling were conducted. RESULTS: Eight psychosocial predictors of physical activity were identified. Self-efficacy (r+ = 0.26) and intentions (r+ = 0.33) were the strongest predictors in bivariate analyses. The structural equation models included attitudes, injunctive norms, self-efficacy, intentions, and physical activity (k = 22, N = 4,385). The model with the best fit, χ2(2) = 0.11, p = .95, root mean square error of approximation = .00, comparative fit index = 1.00, Tucker-Lewis index = 1.00, indicated that all specified paths were significant. Intentions were the strongest predictor of physical activity (β = 0.27, p < .001), and attitudes and self-efficacy were strong predictors of intentions (both βs = 0.29, ps < .001). Few significant moderators were observed. CONCLUSION: This review indicates that self-efficacy and intentions are direct predictors of physical activity in cancer survivors. Further, attitudes and norms predict physical activity through intentions. Findings inform intervention development to increase physical activity engagement among cancer survivors. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it