Nurse-led physical health interventions for people with mental illness: an integrative review of international literature
Bibliographic record
Abstract
Background People experiencing mental illness receive physical healthcare from nurses in a variety of settings including acute inpatient, secure extended care, forensic, and community services. While nurse-led clinical practice addressing sub-optimal consumer physical health is salient, a detailed understanding and description of the contribution by nurses to physical health interventions in people experiencing mental illness is not clearly articulated in the literature.Aims The aim of this integrative review is to describe the state of knowledge on nurse-led physical health intervention for consumers, focusing on nursing roles, nursing assessment, and intervention settings.Methods A systematic search of six databases using Medical Subject Headings from 2001 and 2022 inclusive was conducted. The Mixed Methods Appraisal Tool (MMAT) was utilised for quality appraisal.Results Seventy-four studies were identified as “nurse-led”. Interventions were most common among community settings (n = 34, 46%). Nurses performed varied roles, often concurrently, including the collection of 341 physical health outcomes, and multiple roles with 225 distinct nursing actions identified across the included studies. A nurse as lead author was common among the included studies (n = 46, 62%). However, nurses were not always recognised for their efforts or contributions in authorship.Conclusions There is potential gap in role recognition that should be considered when designing and reporting nurse-led physical health interventions.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".