Hospital discharge communication problems in 10 high-income nations: a secondary analysis of an international health policy survey
Bibliographic record
Abstract
OBJECTIVES: We aimed to determine the prevalence of hospital discharge communication problems in adults of 10 high-income nations and the associated factors. DESIGN: Secondary analysis of cross-sectional survey data. SETTING: 2023 Commonwealth Fund International Health Policy Survey for Adults, including data from residents of Australia, Canada, France, Germany, the Netherlands, New Zealand, Sweden, Switzerland, the UK and the USA. PARTICIPANTS: 3763 survey respondents aged 18 and older who reported hospitalisation at least one time in the past 2 years. PRIMARY AND SECONDARY OUTCOME MEASURES: Our primary outcome measure is poor discharge communication (PDC), which is a composite variable comprising three questions regarding the provision of written information, follow-up arrangement and discussion of medications at time of discharge. RESULTS: The overall PDC rate was 17.1%, with the highest in Germany (19.7%) and the lowest in the Netherlands (9.2%). No follow-up arrangement was the most commonly reported problem (22.8%). Respondents who concerned about social service needs and mental health issues were more likely to report PDC. CONCLUSIONS: Providers should consider factors which impact PDC at hospital discharge and tailor communication appropriately. Hospitals, communities and countries should work towards policies that address underlying issues related to social determinants of health, including support for lower-income patients, improved treatment access for patients with physical and mental health conditions, and food and housing stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".