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Record W3143305553 · doi:10.1590/1519-6984.238665

Cutaneous leishmaniasis–Awareness, knowledge and practices among general population in rural and urban areas in Malakand region, Pakistan

2021· article· en· W3143305553 on OpenAlexaboutno aff
Wali Khan, Inamullah Khan, I. Ullah, Siti Nursheena Mohd Zain, Waheed Ali Panhwar, Sardar Azhar Mehmood, Shabir Ahmed, G. B. Siyal, Mushtaq Ahmad, Muzafar Shah, T Rafique, Naushad Ali, M. Salim, Altaf Hussain

Bibliographic record

VenueBrazilian Journal of Biology · 2021
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCutaneous leishmaniasisLeishmaniasisQuarter (Canadian coin)PopulationSocioeconomicsGeographyOutbreakDiseaseEnvironmental healthRural areaRural populationVeterinary medicineEnvironmental protectionMedicineSociologyPathologyArchaeology

Abstract

fetched live from OpenAlex

Malakand region is an endemic area for cutaneous leishmaniasis (CL). However, there are limited number of studies of this disease in Pakistan. Therefore, a study was conducted to understand the level of awareness attitude and practice among the residents of Makaland towards CL and the disease vectors. This study adopted a cross-sectional approach with a total of 400 respondents (n=93 rural and n= 307 urban). Overall, the population in Malakand region (61.2%) were well-informed in the role of sand fly in transmitting diseases, but most lack knowledge on the vector's behavior and almost a quarter (24.5%) were unable to provide knowledge on proper control measures. Alarmingly, the practice and attitudes of the general population was not satisfactory as close to half (49.8%) of the population did adopt any control method. This study calls for increase in awareness through health education campaign to reduce the risk of cutaneous leishmaniasis outbreaks in the future.

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.354
Teacher spread0.327 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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Same venueBrazilian Journal of BiologySame topicResearch on Leishmaniasis StudiesFrench-language works237,207