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Occupational Stress and Job Satisfaction of Public-School Teachers in Distance Education: A Quantitative Analysis

2022· article· en· W4293017567 on OpenAlexaff
Jenneth A. Labrado, Rachel Jane M. Casquejo, Jennifer T. Alterado, Maria Janice T. Alterado

Bibliographic record

VenueInternational Journal of Science and Management Studies (IJSMS) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsLa Cité Collégiale
Fundersnot available
KeywordsJob satisfactionPsychologySchool teachersOccupational stressStress (linguistics)Job stressJob attitudeApplied psychologyMedical educationSocial psychologyJob performanceMathematics educationMedicine

Abstract

fetched live from OpenAlex

The study focused onOccupational Stress and Job Satisfaction of Public-School Teachers in Distance Education using the descriptive correlational technique. Furthermore, the study will look into whether there was a link between the degree of occupational stress, and levels of job satisfaction among the public-school teachers. The study was conducted in the province of Cebu. The respondents of the study are the 90 teachers selected using the systematic Random sampling method. Moreover, the research instrument used was the 36-item Teacher Stress Inventory (TSI) (Schutz and. Long, 1988) and the Minnesota Satisfaction Questionnaire (MSQ-short form) to measure intrinsic and extrinsic job factors of employees. The study yielded that teachers are experiencing some type of stress in the workplace which may be due to the added responsibilities in distance learning. Likewise, despite the stress and the additional obligations brought on by the epidemic, teachers were satisfied with their jobs. In addition, the study found a significant relationship between the Level of Occupational Stress and the Level of Job Satisfaction of Teachers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.046
GPT teacher head0.398
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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