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Pattern and Trend of Deliberate Self‐Harm in Western Nepal

2009· article· en· W2140921587 on OpenAlexaboutno aff
Sonu Hangma Subba, VS Binu, Ritesh G. Menezes, Tanuj Kanchan, Rajkumar Patil, Sadip Pant, Archana Saha, Asis De, Manu Rana

Bibliographic record

VenueJournal of Forensic Sciences · 2009
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuarter (Canadian coin)DemographyHarmOccupational safety and healthInjury preventionSuicide preventionPoison controlCohortEnvironmental healthGeographyPsychologySocial psychology

Abstract

fetched live from OpenAlex

Acts of deliberate self-harm (DSH) not only affect the people directly involved, but also have grave psychological and social impact on the family and community. In the present study, a cohort of 173 cases of DSH reported from April 2002 to March 2005 was retrospectively analyzed, by perusing the medicolegal register maintained by the Emergency Department at the Western Regional Hospital, Pokhara in the Western Development Region of Nepal. The data were entered and analyzed using SPSS Version 10.1. More than two-thirds of total cases were females. About 60% of cases were observed in the age group of 15-24 years. Poisoning (89.6%) was the most preferred method of deliberate self-harm. Organophosphate pesticides were consumed in nearly two-thirds of the poisoning cases. The majority of cases were reported during the months of May to July and had occurred during the last quarter of the day. More than a twofold increase was observed in the frequency of cases during the 3-year study period. The said observations were compared and contrasted with the available literature across the globe. The presentation is concluded by highlighting the limitations encountered in Nepal and the scope to overcome the same.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.171
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.341
Teacher spread0.299 · 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

Citations35
Published2009
Admission routes1
Has abstractyes

Explore more

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