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Record W4313290158 · doi:10.47391/jpma.3120

Violence against health care workers in rural areas of Sindh, Pakistan

2022· article· en· W4313290158 on OpenAlexaff
Saleema Arif, Lubna Baig, Shiraz Shaikh, Ibrahim Hashmi, Zaini Sarwar, Zarrukh Ali Baig

Bibliographic record

VenueJournal of the Pakistan Medical Association · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of SaskatchewanUniversity of Calgary
Fundersnot available
KeywordsMedicineWorkplace violenceHealth careCross-sectional studyHealth professionalsRural areaOccupational safety and healthCluster (spacecraft)Family medicineSuicide preventionEnvironmental healthNursingPoison control

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the magnitude of violence against healthcare workers in a rural setting, and the consequences of this violence on their personal and professional lives. METHODS: The descriptive, quantitative, cross-sectional study was conducted in 4 rural districts of the Sindh province of Pakistan from February to December 2019, and comprised healthcare workers, including doctors, nurses, support staff and field workers. Data was collected using a structured questionnaire. Data was analysed using SPSS 22. RESULTS: Of the 1622 subjects, 929(57.3%) were males and 693(42.7%) were females. The overall mean age was 35.55+/-10.05 years. The largest cluster was that of doctors 396(24.4%), followed by technicians 202(12.5%). Overall, 522(32.2%) subjects had a professional experience of 1-5 years. Violence at workplace in any form was experienced by 693(42.7%) subjects. Verbal violence had been experienced by 396(24.4%) subjects, while 228(14.1%) had witnessed it. The corresponding numbers for physical violence were 122(7.5%) and 22(1.4%). Verbal violence was more prevalent compared to physical violence (p<0.01). The major effect was that the healthcare workers remained alert 537(33.1%), felt frustrated 524(32.3%) and disturbed 503(31%). Also, 272(16.8%) subjects were planning to migrate or quit the profession. CONCLUSIONS: Violence was found to be a significant issue in rural Sindh.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.319
Teacher spread0.312 · 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 teacher head, 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

Explore more

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