An Application of the Technology Acceptance Model to Individual Protective Measures (IPMs) Against Viruses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it