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Record W2136672881 · doi:10.5958/j.2319-5886.2.4.145

Levels of stress amongst the school teachers in a public school of rural Western Maharashtra

2013· article· en· W2136672881 on OpenAlexaff
Rahul Kunkulol, Rusina Karia, Prashant Patel, Abhinav David

Bibliographic record

VenueInternational Journal of Medical Research & Health Sciences · 2013
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsPontifical Institute of Mediaeval Studies
Fundersnot available
KeywordsStress (linguistics)SocioeconomicsSchool teachersRural areaMedicineMedical educationGeographyVeterinary medicinePsychologyMathematics educationSociologyPathology

Abstract

fetched live from OpenAlex

Teachers are among the professions reporting highest level of work-related stress, the study was undertaken to evaluate the levels of stress amongst school teachers in a public school of rural western Maharashtra Prospective survey based study was carried out amongst school teachers of rural western Maharashtra using Copenhagen Psychosocial Questionnaire (COPSOQ). The survey was carried out on 3 scheduled visits over a period of 2 months after the Institutional Ethical committee approval. Total 110 Primary and secondary school teachers, satisfying inclusion and exclusion criteria were randomly selected for the study. All the questions in the Copenhagen Psychosocial Questionnaire (COPSOQ) were graded according to 1 (Always-0), 2 (Sometimes-25), 3 (Often-50), 4 (Seldom-75) and 5 (Never-100). The scale value was calculated as the simple average. More the average score less the stress and vice versa Inability to understand the meaning and importance of work, improper clarity about the job, inability to cope with the problems were found to be the factors always contributing to stress 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)0.000
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.247
GPT teacher head0.545
Teacher spread0.298 · 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
Published2013
Admission routes1
Has abstractyes

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