Initial Psychometric Evidence of Physical Inactivity Perceived Experience Scale (Pipes): COVID-19 Pandemic as a Pilot Study
Bibliographic record
Abstract
Aims: Our study aimed to develop a two-factor self-administered orthogonal questionnaire to assess the experience of perceived physical inactivity, to test its psychometric properties, to confirm its relationships with fear of COVID-19, and finally, with perceived stress during the pandemic. Methods: A total of 481 Tunisian subjects collected in several cities, aged from 16 to 67 years with a mean age = 32.48 ± 9.46, and of both sexes participate in our study with (male: 51.8%) and (female: 48.2%), divided according to the level of study into three categories. All subjects voluntarily answered the PIPES questionnaire, the IPAQ scale, the COVID-19 fear scale and the PSS-10 test. Results: The results of the exploratory and confirmatory factor analysis supported the robustness of the tool measure. In addition, examination of configurational, metric, scalar, and strict invariance supported the equivalence of the structure by gender and educational level. Concurrent validity was established by the positive association of a negative perception of physical inactivity with scores measured by the IPAQ scale and a negative association with scores of COVID-19 fear and perceived stress. Whereas, a positive perception of physical inactivity from the COVID-19 scale was negatively associated with the IPAQ and positively associated with fear of COVID-19 and perceived stress. Conclusion: The PIPES-10 scale can be used to measure the perception of physical inactivity in different situations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".