Health Impact of Long Covid among Student Teachers, Kasetsart University
Bibliographic record
Abstract
This study employed a mixed methods approach, consisting of two distinct phases. Phase 1 aimed to investigate the health consequences of long Covid among student teachers who had been diagnosed with SARS-CoV-2 infection, as well as the factors linked with long Covid problems. The data collection process involved utilizing the Taro Yamane, (1973) formula to determine the sample size, which was conducted with a 95% confidence level. The population under consideration consisted of student teachers in their first to fourth year of study, as specified in the Yamane table. The total sample size was determined to be n=286, and the sampling procedure employed stratification based on the relative proportions of student teachers in each of the four academic levels. Phase 2 refers to the second stage in a process. In order to gain further insights into the enduring health consequences of instructing student teachers at Kasetsart University, this study aims to employ comprehensive interviews with a sample population. The objective is to scrutinize intricate details pertaining to the health ramifications of long Covid, while also explore the adverse effects of long Covid on student teachers at Kasetsart University. Based on the findings, seven categories of symptoms can be explored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".