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Record W2998440316 · doi:10.3390/ijerph17010355

Knowledge and Knowledge Needs about Lyme Disease among Occupational and Recreational Users of the Outdoors

2020· article· en· W2998440316 on OpenAlexafffundabout
Sarah E. St. Pierre, Odette N. Gould, Vett K. Lloyd

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsMount Allison University
FundersMount Allison University
KeywordsRecreationLyme diseasePsychological interventionEnvironmental healthDiseaseSample (material)PsychologyKnowledge levelMedicineGerontologyNursing

Abstract

fetched live from OpenAlex

As the prevalence of Lyme disease increases across Canada, it is imperative that the educational needs of at-risk groups be identified. The current study compared the level of knowledge and the knowledge needs about Lyme disease among individuals that spend time outdoors for work and for recreational purposes. Between December 2018 and February 2019, a survey was distributed to outdoor organizations across New Brunswick, Canada. Within the current sample of 137 individuals, 36% spent time outdoors for their occupation and 64% for recreational activities. Results showed no significant difference between these groups with regard to their level of knowledge, perceived efficacy and performance of various methods of prevention, and educational needs. Overall, the entire sample reported a low level of knowledge about Lyme disease. Participants perceived each prevention behavior to be at least somewhat effective, and behaviors perceived to be more effective were more likely to be carried out, but the performance of the behaviors varied. The most frequently performed behaviors included wearing long pants and protective footwear. Participants identified several aspects of Lyme disease about which they would like to have more information. The findings call attention to the specific needs of at-risk groups that must be considered when developing educational interventions.

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)0.001
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 score0.018
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.055
GPT teacher head0.351
Teacher spread0.296 · 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

Citations24
Published2020
Admission routes3
Has abstractyes

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