Personal Factors and Mental Health of Public School Teachers in Lavezares I District, Division of Northern Samar
Bibliographic record
Abstract
The study focused on the personal factors that influence the mental health of public-school teachers. This was conducted among elementary and secondary teachers in Lavezares I District, Division of Northern Samar. This study employed descriptive-correlational research design involving 30 participants chosen through purposive sampling. Data from survey questionnaire were analyzed using Descriptive Statistics and Pearson Product-Moment Correlation Coefficient. This was conducted within the third quarter of the school year 2021-2022. Findings showed that more of them are between 31–38 years old. Majority of them are female, married, with net take home pay of 5,000 – 11,499, recipients of completed academic requirement (CAR) for their MA, hold teacher III position, have been in the service for 7 years and below, with not more than 2 trainings and seminars related to distance learning attended. In terms of the mental health, results showed that teachers who are at the forefront of distance learning implementation manifest severe stress, moderate anxiety, and mild depression. The demographic profile in terms of age, gender, civil status, net take home pay, highest educational attainment, teaching position, length of service, and the number of attended trainings and seminars related to distance education found not significantly correlated teachers’ mental health. The findings of this study will provide input how school can address personal factors and mental health issues of teachers to become resilient in the face of adversities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".