Chest mobility and health-related quality of life in students during the COVID-19 pandemic
Bibliographic record
Abstract
Background: Over the past ten years, there has been a growing trend in utilizing non-invasive techniques for monitoring respiratory function, with a particular emphasis on assessing chest expansion during both maximum inhalation and exhalation, which have gained popularity as research tools.The existing research conducted unequivocally affirms that the coronavirus disease 2019 (COVID-19) pandemic has had a substantial impact on multiple dimensions of Health-Related Quality of Life (HRQoL).Aims: This study aimed to assess chest mobility and HRQoL among female students at the Faculty of Physical Rehabilitation, University of Physical Education in Kraków, using an online approach amidst the backdrop of the COVID-19 pandemic. Material and methods:In a research project that included 40 women with an average age of 22.75 ± 1.51 years and an average BMI of 22.15 ± 3.81 kg/m 2 , self-reported chest mobility was remotely gauged using a measuring tape, while HRQoL was evaluated utilizing the 15D instrument.In order to perform the survey while maintaining the hygiene rules during the COVID-19 pandemic, an individual interview was conducted with each respondent using Microsoft Teams.The 15D questionnaire was then sent to the female students as a Google form.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".