Building patient capacity to participate in care during hospitalisation: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To map the existing literature and describe interventions aimed at building the capacity of patients to participate in care during hospitalisation by: (1) describing and categorising the aspects of care targeted by these interventions and (2) identifying the behaviour change techniques (BCTs) used in these interventions. A patient representative participated in all aspects of this project. DESIGN: Scoping review. DATA SOURCES: MEDLINE, Embase and CINAHL (Inception -2017). STUDY SELECTION: Studies reporting primary research studies on building the capacity of hospitalised adult patients to participate in care which described or included one or more structured or systematic interventions and described the outcomes for at least the key stakeholder group were included. DATA EXTRACTION: Title and abstract screening and full text screening were conducted by pairs of trained reviewers. One reviewer extracted data, which were verified by a second reviewer. Interventions were classified according to seven aspects of care relevant to hospital settings. BCTs identified in the articles were assigned through consensus of three reviewers. RESULTS: Database searches yielded a total 9899 articles, resulting in 87 articles that met the inclusion criteria. Interventions directed at building patient capacity to participate in care while hospitalised were categorised as those related to improving: patient safety (20.9%); care coordination (5.7%); effective treatment (5.7%) and/or patient-centred care using: bedside nursing handovers (5.7%); communication (29.1%); care planning (14%) or the care environment (19.8%). The majority of studies reported one or more positive outcomes from the defined intervention. Adding new elements (objects) to the environment and restructuring the social and/or physical environment were the most frequently identified BCTs. CONCLUSIONS: The majority of studies to build capacity for participation in care report one or more positive outcomes, although a more comprehensive analysis is warranted.
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 | 0.056 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.035 | 0.035 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".