Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care
Bibliographic record
Abstract
BACKGROUND: Studies have shown that telehealth applications in palliative care are feasible, can improve quality of care, and reduce costs but few studies have focused on user acceptance of current technology applications in palliative care. Furthermore, the perspectives of health administrators have not been explored in palliative care and yet they are often heavily involved, alongside providers, in the coordination and use of health technologies. The study aim was to explore both health care provider and administrator perceptions regarding the usefulness and ease of using technology in palliative care. METHODS: The Technology Acceptance Model (TAM) was used as the guiding theoretical framework to provide insight into two key determinants that influence user acceptance of technology (perceived usefulness and ease of use). Semi-structured interviews (n = 18) with health providers and administrators with experience coordinating or using technology in palliative care explored the usefulness of technologies in palliative care and recommendations to support adoption. Interview data were analyzed using inductive thematic analysis to identify common, meaningful themes. RESULTS: Four themes were identified; themes related to perceived usefulness were: enabling remote connection and information-sharing platform. Themes surrounding ease of use included: integration with existing IT systems and user-friendly with ready access to technical support. Telehealth can enable remote connection between patients and providers to help address insufficiencies in the current palliative care environment. Telehealth, as an information sharing platform, could support the coordination and collaboration of interdisciplinary providers caring for patients with palliative needs. However, health technologies need to passively integrate with existing IT systems to enhance providers' workflow and productivity. User-friendliness with ready access to technical support was considered especially important in palliative care as patients often experience diminished function. CONCLUSION: Participants' perspectives of technology acceptance in palliative care were largely dependent on their potential to help address major challenges in the field without imposing significant burden on providers and patients.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".