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Record W1701318927 · doi:10.1186/1471-2458-14-1302

A focus group study of enteric disease case investigation: successful techniques utilized and barriers experienced from the perspective of expert disease investigators

2014· article· en· W1701318927 on OpenAlexaffabout
Stanley Ing, Christina Lee, Dean Middleton, Rachel Savage, Stephen Moore, Doug Sider

Bibliographic record

VenueBMC Public Health · 2014
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsMcMaster UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineFocus groupInterviewPublic healthMisinformationQualitative researchDiseaseMedical educationFamily medicineNursingPathologyComputer security

Abstract

fetched live from OpenAlex

BACKGROUND: In Ontario, Canada, enteric case investigators perform a number of functions when conducting telephone interviews including providing health education, collecting data for regulatory purposes ultimately to prevent further illness, enforcement, illness source attribution and outbreak detection. Information collected must be of high quality as it may be used to inform decisions about public health actions that could have significant consequences such as excluding a person from work, recalling a food item that is deemed to be a health hazard, and/or litigations. The purpose of this study was to describe, from the perspectives of expert investigators, barriers experienced and the techniques used to overcome these barriers during investigation of enteric disease cases. METHODS: Twenty eight expert enteric investigators participated in one of four focus groups via teleconference. Expert investigators were identified based on their ability to 1) consistently obtain high quality data from cases 2) achieve a high rate of completion of case investigation questionnaires, 3) identify the most likely source of the disease-causing agent, and 4) identify any possible links between cases. Qualitative data analysis was used to identify themes pertaining to successful techniques used and barriers experienced in interviewing enteric cases. RESULTS: Numerous barriers and strategies were identified under the following categories: case investigation preparation and case communication, establishing rapport, source identification, education to prevent disease transmission, exclusion, and linking cases. Unique challenges experienced by interviewers were how to collect accurate exposure data and educate cases in the face of misconceptions about enteric illness, as well as how to address tensions created by their enforcement role. Various strategies were used by interviewers to build rapport and to enhance the quality of data collected. CONCLUSIONS: To our knowledge, this is the first study to examine the perspectives of expert enteric disease case investigators on successful interview techniques and barriers experienced during enteric case investigation. A number of recommendations could improve the process of enteric case investigation in the Ontario context which include formal training and development of resource materials pertaining to interviewing, standardized interviewing tools, strategies to address cultural and language barriers, and the implementation of the single interviewer approach.

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)0.005
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 score0.081
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.329
Teacher spread0.292 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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