Relationship between management and resilience in healthcare: a study protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Management has been identified as a critical component of organisational resilience when responding to adverse events and crises, as managers must ensure an effective operational response and provide direction and guidance to teams. While there are many management approaches, strategies and interventions that have been applied and studied in healthcare, the impact of them in relation to resilience in healthcare has not been explored, particularly at the organisational level. Understanding the impact of management approaches, strategies and interventions on resilience has the potential to inform healthcare organisations on how to better use management to prepare and respond to organisational adverse events. The objective of this mixed-methods systematic review is to understand the relationship between management and organisational resilience in healthcare, including management approaches and strategies that promote resilience in healthcare. METHODS AND ANALYSIS: A search through MEDLINE, Cumulative Index to Nursing and Allied Health Literature, PubMed and EMBASE will be conducted between 1 August 2021 and 31 December 21. This review will consider empirical quantitative, qualitative and mixed-methods studies published in English from 2010 to the present that meet the inclusion and exclusion criteria. Selected studies will be assessed in detail and extracted data will be reviewed by two independent reviewers. Results of the search will be reported in full in the final systematic review and presented in a Preferred Reporting Items for Systematic Reviews and Meta-Analysis flow diagram. This review will follow a convergent integrated approach to data synthesis and integration. ETHICS AND DISSEMINATION: This systematic literature review includes no collection of primary data; hence ethical approval will not be sought. The outcomes from this review will be disseminated in a peer-reviewed journal, as conference presentation, and as condensed summary for managers in healthcare and policy-makers. PROSPERO REGISTRATION NUMBER: CRD42020223362.
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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 | 0.137 | 0.122 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.019 |
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".