Adherence of those at low risk of disease to public health measures during the COVID-19 pandemic: A qualitative study
Bibliographic record
Abstract
Public health measures (PHMs) proactively and reactively reduce the spread of disease. While these measures target individual behaviour, they require broad adherence to be effective. Consequently, the World Health Organization issued a special appeal to young adults, a known non-adherent population, for increased adherence with COVID-19 guidelines. However, little is known about why these low-risk individuals do or do not adhere to PHMs. This study investigates why young adults in a low-risk setting adhered to PHMs implemented during the COVID-19 pandemic. A qualitative research approach was chosen to gain an in-depth understanding of participants' thoughts and experiences related to PHM adherence. Semi-structured interviews were conducted in April-May 2021 with 30 young adults living in Prince Edward Island (PEI), the province with the lowest COVID-19 case rate in Canada at that time. Thematic analysis was used to create a codebook based on the Theoretical Domains Framework, which was then inductively modified. The analysis identified eight themes that explained the adherence of young adults: (1) clear, purpose-driven adherence rationale, (2) developing trust in the local leadership, (3) adapting to novel measures, (4) manageable disruption, (5) adhering to reduce anxiety, (6) collective duty towards one's community, (7) moral culpability and (8) using caution rather than compliance. Together, these themes demonstrate that young adults adhered to PHMs because of their sense of connection to their community, public health leadership, and concerns over stigma. We further argue that clear guidelines and communication from public health officials during both periods of high and low COVID-19 cases facilitate adherence. These findings are important for mitigating future public health emergencies as they explain why young adults, an important segment of the population whose adherence is critical to the success of PHMs, follow PHMs. Further, these findings can inform public health officials and other stakeholders aiming to develop successful adherence strategies.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".