Discharge planning: Narrated by nursing staff in primary healthcare and their concerns about using video conferencing in the planning session – An interview study
Bibliographic record
Abstract
Background/Objective: This paper sets out to describe experience-based reflections on discharge planning as narrated by nursing staff in primary healthcare, along with their concerns about how the introduction of video conferencing might influence the discharge planning situation. Methods: Interviews were conducted with nursing staff working at a primary healthcare centre in South East Sweden. Each interview took place was conducted on a one-to-one basis in dialogue form, using open questions and supported by an interview guide. It was then analysed using a phenomenological hermeneutic method. Participants were eligible for the study if they had given their informed consent and if they worked with discharge planning and home-based healthcare provision. In total, 10 of the 30 persons working at the primary healthcare centre participated in the study. Results: It was found that nursing staff in primary healthcare regarded the planning session as stressful, time-consuming and characterised by a lack of respect between nursing staff at the hospital and nursing staff in primary healthcare. They also described uncertainty and hesitation about using video conferences where patients might probably be the losers and nursing staff the winners. Conclusions: It is suggested that there is a need for improvement in communication and understanding between nursing staff at the hospital and nursing staff in primary healthcare in order to develop discharge planning. There is also a need for the nursing staff in primary healthcare to obtain more information about how Information Technology (IT) solutions could support their work and help them to find ways to collaborate.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".