Overseas Filipino Workers' Employment Compliance, Difficulties, and Job Satisfaction in the Middle East
Bibliographic record
Abstract
Abstract: This study aimed to determine the extent of OFWs’ employment compliance, level of difficulties, and level of job satisfaction in the Middle East during the last quarter of the Calendar Year 2022. A quantitative research design was applied. It was conducted in a District of GCC in the Middle East. The 100 respondents from the two Occupational Classifications (Professionals and Non-professionals) of the four (4) private Organizational Groups of OFWs’ answered the Survey Questionnaire paper-based. Content areas cover staff qualification, government requirements, contract requirements, life experience abroad, accommodation, transportation, safety, working hours and working conditions, compensation, benefits, job security and tenure, recognition, reward, promotions, and professional development opportunities. The data collection was gathered with permission from the group leaders. Statistical tools were processed through SPSS. Frequency count was used for profiling, Mann-Whitney U Test was used for Comparative analysis, and Spearman rho Test was used for nonparametric correlational analysis. The extent of the OFWs' Employment Compliance in relation to the level of Difficulties was significant. The extent of employment compliance in relation to the level of Job Satisfaction was significant. And the level of the Difficulties in relation to Job Satisfaction was significant. Dependent and Independent variables rely on each other as part of a whole package for OFWs' working in the Middle East. Keywords: OFW, Middle East, Employment Compliance, Difficulties, Job Satisfaction
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".