A tale of two models: Changes in psychological need satisfaction and physical activity over 3 years.
Bibliographic record
Abstract
OBJECTIVE: (a) Examine longitudinal measurement invariance of scores from psychological need satisfaction (PNS) scales, and (b) examine if changes in PNS were associated with change in moderate-to-vigorous physical activity (MVPA). METHOD: Adolescents (N = 842, Mage = 10.8, SD = .6) enrolled in the Monitoring Activities of Teenagers to Comprehend their Habits (MATCH) study completed measures of PNS and MVPA every 4 months over a 3-year period (2011-14) for a total of 9 times. RESULTS: PNS scores demonstrated strong longitudinal measurement invariance (i.e., invariant factor loadings and intercepts). Latent growth curve modeling indicated that a factor representing perceptions of all 3 PNS variables was positively associated with MVPA at Time 1 (β = .562, p < .05), and that increases in the common PNS factor were associated with increases in MVPA (β = .545, p < .05) with a large effect size (Rinitial MVPA2 = .316; Rchange in MVPA2 = .301). In an alternative model, MVPA at Time 1 was associated with perceived common PNS at Time 1 (β = .602, p < .001), and increases in MVPA were associated with increases in common PNS (β = .667, p < .001) with a large effect size (Rinitial PNS2 = .363 of the Rchange in PNS2 = .426). CONCLUSIONS: Longitudinal measurement invariance was supported, and therefore PNS scores could be used to study change over time. Further, 2 equally well fitting models were found suggesting that change in PNS can be both an antecedent and an outcome of MVPA. As such, both PNS and MVPA could be targeted in interventions aimed at increasing need satisfaction or MVPA.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".