A focus group study of enteric disease case investigation: successful techniques utilized and barriers experienced from the perspective of expert disease investigators
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".