Factors affecting front line staff acceptance of telehealth technologies: a mixed‐method systematic review
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
AIM: To synthesize qualitative and quantitative evidence of front-line staff acceptance of the use of telehealth technologies for the management of Chronic Obstructive Pulmonary Disease and Chronic Heart Failure. BACKGROUND: The implementation of telehealth at scale is a governmental priority in countries including the UK, USA and Canada, but little research has been conducted to analyse the impact of implementation on front-line nursing staff. DATA SOURCES: Six relevant data bases were searched between 2000-2012. DESIGN: Mixed-method systematic review including all study designs. REVIEW METHODS: Centre for Reviews and Dissemination approach with thematic analysis and narrative synthesis of results. RESULTS: Fourteen studies met the review inclusion criteria; 2 quantitative surveys, 2 mixed-method studies and 10 using qualitative methods, including focus groups, interviews, document analysis and observations. Identified factors affecting staff acceptance centred on the negative impact of service change, staff-patient interaction, credibility and autonomy, and technical issues. Studies often contrasted staff and patient perspectives, and data about staff acceptance were collected as part of a wider study, rather than being the focus of data collection, meaning data about staff acceptance were limited. CONCLUSION: If telehealth is to be implemented, studies indicate that the lack of acceptance of this new way of working may be a key barrier. However, recommendations have not moved beyond barrier identification to recognizing solutions that might be implemented by front-line staff. Such solutions are imperative if future roll-out of telehealth technologies is to be successfully achieved.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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