Discharge planning in mental healthcare settings: A review and concept analysis
Bibliographic record
Abstract
To ensure a safe transition of mental health patients from hospital to community settings, greater attention is being given to discharge planning. However, assessing the quality of discharge planning has been challenging due to wide variations in its definition. To facilitate evaluation of discharge planning, its meaning in the mental health literature was systematically explored. This concept analysis is part of a larger study to develop an instrument to measure the quality of discharge planning processes in mental health care. Walker and Avant's (2011) concept analysis approach was adopted to provide a comprehensive definition of discharge planning. Electronic databases and grey literature were searched and analysed according to Grant and Booth's (2009) systematic search and review process. Literature published between 1900 and 2018 was reviewed. Forty-nine articles meeting the inclusion criteria were included in the analysis. Discharge planning is a complex, multifaceted concept with six defining attributes: comprehensive needs assessment; collaborative, patient-centered care; resource availability management; care and service coordination; discharge planner role; and a discharge plan. Discharge planning begins with the initial rapid assessment and symptom stabilization of a patient on admission, coincides with treatment planning, and is associated with hospital readmissions and continuity of care. The mental health literature was reviewed to analyse different interpretations of discharge planning. The conceptual definition provided can assist healthcare providers, organizational leaders, and policymakers to design and implement effective discharge planning policies and guidelines. Providing clarity regarding discharge planning also provides a critical foundation for developing an instrument.
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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".