More than just staffing? Assessing evidence on the complex interplay among nurse staffing, other features of organisational context and resident outcomes in long-term care: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Especially in acute care, evidence points to an association between care staffing and resident outcomes. However, this evidence is more limited in residential long-term care (LTC). Due to fundamental differences in the population of care recipients, organisational processes and staffing models, studies in acute care may not be applicable to LTC settings. We especially lack evidence on the complex interplay among nurse staffing and organisational context factors such as leadership, work culture or communication, and how these complex interactions influence resident outcomes. Our systematic review will identify and synthesise the available evidence on how nurse staffing and organisational context in residential LTC interact and how this impacts resident outcomes. METHODS AND ANALYSIS: We will systematically search the databases MEDLINE, EMBASE, CINAHL, Scopus and PsycINFO from inception for quantitative research studies and systematically conducted reviews that statistically modelled interactions among nurse staffing and organisational context variables. We will include original studies that included nurse staffing and organisational context in LTC as independent variables, modelled interactions between these variables and described associations of these interactions with resident outcomes. Two reviewers will independently screen titles/abstracts and full texts for inclusion. They will also screen contents of key journals, publications of key authors and reference lists of all included studies. Discrepancies at any stage of the process will be resolved by consensus. Data extraction will be performed by one research team member and checked by a second team member. Two reviewers will independently assess the methodological quality of included studies using four validated checklists appropriate for different research designs. We will conduct a meta-analysis if pooling is possible. Otherwise, we will synthesise results using thematic analysis and vote counting. ETHICS AND DISSEMINATION: Ethical approval is not required as this project does not involve primary data collection. The results of this study will be disseminated via peer-reviewed publications and conference presentation. PROSPERO REGISTRATION NUMBER: CRD42021272671.
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 | 0.180 | 0.194 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.022 | 0.017 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".