Hospital discharge planning: A systematic literature review on the support measures that social workers undertake to facilitate older patients' transition from hospital admission back to the community
Bibliographic record
Abstract
OBJECTIVES: To systematically review the literature on measures social workers undertake to facilitate discharge planning for older people in a resource-scarce environment. METHODS: Systematic search of electronic databases for peer-reviewed articles published in English between January 1990 and August 2020. Articles on hospital discharge planning facilitated by social workers for older patients returning home from hospital admission were included. The Mixed Method Appraisal Tool (MMAT) was used to assess quality and risk of bias. The systematic literature review protocol has been registered with PROSPERO on 27 August 2021. RESULTS: Six studies from Canada and the United States met the eligibility criteria. The most common support measures employed by hospital social workers when discharge planning for older patients were assessment, education, care co-ordination, liaison and engagement with families and providers, conflict resolution, counselling and postdischarge follow-up. Barriers to effective discharge planning were medical complexity, lack of communication, time constraints, limited family support, availability of resources and patient safety. These studies were published between 1993 and 2014 and were not within the Australian context. CONCLUSIONS: There are limited studies on Social Work discharge planning within the Australian context, particularly on how this important service has been impacted by recent aged care reforms. More research on the topic is necessary to fully understand how aged care reforms such as the National Prioritisation System for Home Care Packages have influenced hospital discharge planning and how social workers have adapted their practice to this challenge.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".