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Record W2161585382 · doi:10.5430/jha.v2n2p71

Occupational stress and addiction: Possible neurobiological elucidation of medical waste related individuals

2013· article· en· W2161585382 on OpenAlexvenueno aff
Masum A. Patwary, Lucina Q. Uddin, M. A. Jalil Miah, Mosharraf H. Sarker

Bibliographic record

VenueJournal of Hospital Administration · 2013
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsnot available
FundersTeesside University
KeywordsAddictionStressorContext (archaeology)Medical wastePsychologyDrugClinical psychologyPsychiatryMedicineEnvironmental healthGeographyWaste management

Abstract

fetched live from OpenAlex

Multiple factors contribute to the tendency to develop drug addictions, including social or psychological stressors. Most studies examining causes of and treatments for drug addiction have been conducted in Western developed nations. Here we used phenomenological research approach to explore the neurobiological explanation of drug addiction and to investigate attitudes towards drug use amongst individuals working with medical waste. Data were collected in Dhaka, the capital of Bangladesh, using a variety of techniques based on formal representative sampling for fixed populations and adaptive sampling for roaming populations. We found that over half of participants interviewed reported using illicit substances to cope with occupational stress. Self-reported disease symptoms related to stress were reported by most of the respondents. Working with horrifying waste contributes to increased stress among the participants. These results indicate that in the case of these workers, workplace stressors along with cultural and socio-economic context uniquely contribute to, and exacerbate, tendencies toward drug addiction.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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