Factors affecting front line staff acceptance of telehealth technologies: a mixed‐method systematic review
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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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 | 0.053 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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".