An Application of the Technology Acceptance Model to Individual Protective Measures (IPMs) Against Viruses
Bibliographic record
Abstract
This presentation describes Technology Acceptance Model (TAM) when using individual protective measures (IPMs) against the spreading of viruses like COVID-19. The constructs in TAM are perceived usefulness, and ease of use, attitude towards the use of IPMs and the actual use as well as social influence, which were measured with relevant indicator variables. The statistical method used in the analysis was Partial Least Squares Structural Equation Modelling (PLS-SEM). IPMs include personal protective measures for everyday use (e.g., voluntary home isolation, respiratory etiquette, and hand hygiene); Personal protective measures for influenza pandemics (e.g., voluntary home quarantine, and use of face masks in community settings); and Environmental measures (e.g., routine cleaning of frequently touched surfaces). The results indicate that all relationships were significant also so that the effect sizes were large to medium with the exception of social influence -> perceived usefulness and social influence -> attitude towards usage.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".