Testing delay in an environment of low COVID-19 prevalence: A qualitative study of testing behaviour amongst symptomatic South Australians
Bibliographic record
Abstract
South Australia has to date (October 2021) been highly successful in maintaining an aggressive suppression strategy for the management of the COVID-19 pandemic. However, continued success of this strategy is dependent on ongoing testing by people with symptoms of COVID-19 to identify, trace and quarantine emergent cases as soon as possible. This study sought to explore community members’ decisions about having COVID-19 testing in an environment of low prevalence, specifically exploring their decision-making related to symptoms. This study drew on a qualitative case study design, involving five focus groups, conducted in May 2021, with 29 individuals who had experienced COVID-19-like symptoms since the commencement of testing in South Australia. Participants detailed their last COVID-19-like illness episode and described their decision-making regarding testing. Data collection methods and analysis were theoretically informed by the capability, opportunity, and motivation behaviour (COM-B) model. Participants' belief that COVID-19 symptoms would be ‘unusual’, severe, and persistent caused them to either reject or delay testing. Participants generally employed ‘watch and wait’ and social distancing behaviour rather than timely presentation to testing. Concern about economic loss associated with isolating after testing, and the potential for illness transmission at testing centres further prevented testing for some participants. In a low COVID-19 prevalence environment, individuals rely on pre-existing strategies for interpreting and managing personal illness (such as delaying help seeking if symptoms are mild), which generally conflict with public health management advice about COVID-19. In low prevalence environments therefore public health authorities must give the public a reason to test beyond considerations of personal risk, and clearly communicate the need for ongoing COVID-19 surveillance despite the low prevalence environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".