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Incidence of Low Back Pain and Disability among Postal Office Workers by using Quebec Back Pain Disability Scale

2025· article· W4416021460 on OpenAlexaboutno aff
Swarnima Roychoudhary, Rutuja Kamble, Pranjal Grover

Bibliographic record

VenueInternational Journal For Multidisciplinary Research · 2025
Typearticle
Language
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsLow back painOffice workersIncidence (geometry)SittingMusculoskeletal painBack painPsychological interventionMusculoskeletal disorder

Abstract

fetched live from OpenAlex

The purpose of this research was to assess the incidence of low back pain disability among postal office workers by using the Quebec Back Pain Disability Scale (QBPDS). The present study was also to understand about how this discomfort affected their daily activities and work performance. The Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) was used to assess the frequency, severity, and extent to which musculoskeletal discomfort interfered with their work in the past week. The research was conducted among the postal office worker in several region of Navi Mumbai, total 80 participants were voluntarily participated, self-administered questionnaires and data collection sheet were given. The data were statistically analyzed using MS excel. The incidence of low-back pain among postal office workers was high, due to their nature of job which requires prolonged sitting in inappropriate postures, use of computer frequently and lack of physical exercise. Therefore, workplace ergonomics measure and therapeutic interventions are recommended to minimize the burden of low back pain among postal office workers.

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 score0.580
Threshold uncertainty score0.845

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.416
Teacher spread0.380 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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