Evaluation of teledermatology adoption by health-care professionals using a modified Technology Acceptance Model
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
We examined the main factors affecting the intention of physicians to use teledermatology using a modified Technology Acceptance Model (TAM). The investigation was carried out during a teledermatology pilot study conducted in Spain. A total of 276 questionnaires were sent to physicians by email and 171 responded (62%). Cronbach's alpha was acceptably high for all constructs. Theoretical variables were well correlated with each other and with the dependent variable (Intention to Use). Logistic regression indicated that the original TAM model was good at predicting physicians' intention to use teledermatology and that the variables Perceived Usefulness and Perceived Ease of Use were both significant (odds ratios of 8.4 and 7.4, respectively). When other theoretical variables were added, the model was still significant and it also became more powerful. However, the only significant predictor in the modified model was Facilitators with an odds ratio of 9.9. Thus the TAM was good at predicting physicians' intention to use teledermatology. However, the most important variable was the perception of Facilitators to using the technology (e.g. infrastructure, training and support).
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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