The discharge of individuals from hospital: Do we need to refocus our research?
Bibliographic record
Abstract
Safe and timely discharge of individuals from hospital into the community continues to face a number of barriers. This is despite concerted research efforts to gain better understanding of processes of, and outcomes from, various discharge initiatives. During the doctoral research by the principal author, as with any research, more questions than answers were identified. One of the findings from this research, which incorporated both primary and secondary research methods, raised the question of the direction of future research into the discharge process of individuals from hospital. This question goes to the heart of discharge research, that is, is it time to consider researching discharge interventions which are multi-pronged and take a systems-wide approach? Such interventions need to actively engage policymakers, hospitals, community services and individuals, in order to develop possible solutions to the ongoing barriers which appear to have hindered improvements in the discharge process for over 30 years.
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 | 0.303 | 0.574 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.024 | 0.060 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.018 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".