Social Support and Work Engagement Among Secondary School Teachers in Hong Kong
Bibliographic record
Abstract
Abstract—This study investigated work engagement and social support for 386 secondary school teachers in Hong Kong. It was driven by highly documented issues regarding their below-optimal physical and psychological well-being, which degrades personal well-being as well as the quality of education. Not only was it the aim of this research to quantitatively quantify these two variables, but also to establish a proactive approach to establish how to cultivate positive psychological strengths to maximise engagement. The goal was to provide a pathway for improving the quality of life and overall professional performance of teachers, creating a circle of virtuousness to the advantage of both students and teachers. Data was collected via well-designed web questionnaires and were processed using a stringent statistical approach. The key results offered a mixed and eye-opening picture: while a promising 63.7% of teachers demonstrated social support scores above the median, a much lower but disturbing percentage (43.3%) reflected high work engagement. This disconnection between support systems and engagement suggests that support systems per se are in place, yet perhaps not being maximally applied or to optimal targets. Most notably, the researchers have identified a moderate positive correlation, statistically confirming that greater perceived social support is associated in concrete terms with enhanced work engagement. This turns the finding from anecdotal observation to empirical fact. The relevance of the study transcends mere academic diagnosis to providing actionable intelligence for principals and policymakers alike. It emphasizes the sheer necessity to intentionally strengthen structural and affective support systems and, simultaneously, try to remove the underlying psychological health barriers facing teachers. These findings are highly relevant to teacher training colleges in course design, for school administration in setting supportive working cultures, and to the broader public that relies on a secure, motivated teaching profession.
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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 | 0.001 | 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".