The Impact of Perceived Effectiveness of Non-Pharmaceutical Interventions (NPIs) on Attitude Toward Usage, Behavioral Intentions, and Actual Usage
Bibliographic record
Abstract
The purpose is to examine the impact of the perceived effectiveness of NPIs (e.g., hand hygiene, respiratory etiquette, face masks) on behavioral intentions, attitudes toward usage, and actual use against the backdrop of the Technology Acceptance Model (TAM). Responses were gathered with a survey instrument from Canadian respondents ( N = 278). PLS-SEM and exploratory factor analysis (EFA) were used as analytical methods. The hypotheses between the key constructs were accepted consistently with TAM. Also, the results show a positive relationship between perceived effectiveness and attitude toward the usage of NPIs. However, the perceived effectiveness did not significantly impact behavioral intentions and actual use of NPIs. A significant indirect relationship was discovered between perceived effectiveness via attitudes on behavioral intentions and the actual use of NPIs. The perceptions of the respondents who perceived the NPIs to be effective and those who did not were quite similar. The current research provides a framework for effectively promoting the relevant behaviors while utilizing the Technology Acceptance Model (TAM) framework. The critical role of attitude toward the use of NPIs is highlighted through the direct impact of perceived effectiveness and the indirect effect of perceived effectiveness on behavioral intentions and actual usage toward the use of NPIs.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".