The role of user centric measures in the use of non-pharmaceutical interventions (NPIs)
Bibliographic record
Abstract
Purpose Health authorities have introduced non-pharmaceutical interventions (NPIs) with the aim of reducing the spread of viruses. Against the backdrop of social marketing, normative and utility theories, the purpose of the paper is to examine the relationships between user centric measures such as perceived effectiveness, user satisfaction, and value for effort on intentions to continue to use NPIs. Furthermore, the moderating role of value for effort on user satisfaction and, subsequently, intentions to continue to use NPIs was also considered. Design/methodology/approach A cross-sectional online survey was completed in British Columbia, Canada (N = 287). Analysis was done with partial least squares structural equation modeling. Findings The results show that the relationships between user centric measures are positive and significant on intentions to continue to use NPIs. Furthermore, value for effort moderated the relationship between user satisfaction and intentions to continue to use NPIs – but the relationship was negative. Thus, the higher values of the value for effort construct cause the relationship between user satisfaction and reuse intention to somewhat diminish. Originality/value The results confirm the positive and significant relationships between user centric measures in the context of the use of NPIs and introduce a new understanding of the effect of value for effort on the relationship between user satisfaction and intentions to use NPIs. This enables health officials to better understand how to encourage the use of NPIs.
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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 | 0.036 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".