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Record W4235766792 · doi:10.32920/14637552.v1

Bird harvesting practices and knowledge, risk perceptions, and attitudes regarding avian influenza among Canadian First Nations subsistence hunters: implications for influenza pandemic plans

2021· preprint· en· W4235766792 on OpenAlexafffundabout
Nadia Ali Muhammad Ali Charania, Ian Martin, Eric N. Liberda, Richard Meldrum, Leonard J. S. Tsuji

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoUniversity of Waterloo
FundersAboriginal Affairs and Northern Development CanadaCanadian Institutes of Health Research
KeywordsSubsistence agricultureInfluenza A virus subtype H5N1GeographyPandemicVeterinary medicineRisk perceptionSocioeconomicsDemographyEnvironmental healthBiologyPerceptionAgricultureCoronavirus disease 2019 (COVID-19)MedicineArchaeologyVirologyVirus

Abstract

fetched live from OpenAlex

Background There is concern of avian influenza virus (AIV) infections in humans. Subsistence hunters may be a potential risk group for AIV infections as they frequently come into close contact with wild birds and the aquatic habitats of birds while harvesting. This study aimed to examine if knowledge and risk perception of avian influenza influenced the use of protective measures and attitudes about hunting influenza-infected birds among subsistence hunters. Methods Using a community-based participatory research approach, a cross-sectional survey was conducted with current subsistence hunters (n = 106) residing in a remote and isolated First Nations community in northern Ontario, Canada from November 10–25, 2013. Simple descriptive statistics, cross-tabulations, and analysis of variance (ANOVA) were used to examine the distributions and relationships between variables. Written responses were deductively analyzed. Results ANOVA showed that males hunted significantly more birds per year than did females (F1,96 = 12.1; p = 0.001) and that those who hunted significantly more days per year did not perceive a risk of AIV infection (F1,94 = 4.4; p = 0.040). Hunters engaged in bird harvesting practices that could expose them to AIVs, namely by cleaning, plucking, and gutting birds and having direct contact with water. It was reported that 18 (17.0%) hunters wore gloves and 2 (1.9%) hunters wore goggles while processing birds. The majority of hunters washed their hands (n = 105; 99.1%) and sanitized their equipment (n = 69; 65.1%) after processing birds. More than half of the participants reported being aware of avian influenza, while almost one third perceived a risk of AIV infection while harvesting birds. Participants aware of avian influenza were more likely to perceive a risk of AIV infection while harvesting birds. Our results suggest that knowledge positively influenced the use of a recommended protective measure. Regarding attitudes, the frequency of participants who would cease harvesting birds was highest if avian influenza was detected in regional birds (n = 55; 51.9%). Conclusions Our study indicated a need for more education about avian influenza and precautionary behaviours that are culturally-appropriate. First Nations subsistence hunters should be considered an avian influenza risk group and have associated special considerations included in future influenza pandemic plans.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.087
GPT teacher head0.387
Teacher spread0.301 · 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

Citations1
Published2021
Admission routes3
Has abstractyes

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