Healthcare professionals’ perspectives on technology in transitional care: A multisite qualitative study on current practices, challenges, and future directions
Bibliographic record
Abstract
Objectives: This study aimed to explore healthcare professionals’ views on the use of technology in adult transitional care, identifying challenges, critical procedures, and enabling factors for adoption. Methods: This was a prospective, multisite qualitative study. Data were collected through semi-structured co-creation sessions that explored two main themes, clinical decision-making and technology use in transitional care, through the lenses of current practices, challenges, and future directions. Data were analysed using constant comparison analysis by three independent researchers through iterative open, axial, and selective coding, followed by an impact relationship analysis to explore interconnections between themes. Results: Eleven co-creation sessions were held involving 115 participants. Findings highlight five key transitional care processes, beginning with patient assessment and evaluation, continuing through discharge planning and adherence to protocols, and extending to post-discharge support and follow-up care. The results show how technology can enhance each of these steps by improving digital literacy, user-friendliness, interoperability, and the flow of information. Cross-cutting barriers such as limited resources, privacy concerns, and lack of trust in technology were also identified. Conclusions: Technological tools can support various aspects and processes of transitional care, but their effective adoption requires a distinct set of strategies to address the multiple and complex factors involved.
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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 | 0.030 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".