Evidence map›Paper›PMID 36068564›Full record

ArticleBMC health services research2022

Understanding complex work using an extension of the resilience CARE model: an ethnographic study.

Natalie Sanford, Mary Lavelle, Ola Markiewicz, Gabriel Reedy, Anne Marie Rafferty, Ara Darzi, Janet E Anderson

Abstract read
In one paragraph

Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Resilience practices of healthcare professionals during the hospital discharge process: a qualitative study.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2026
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  4. 'They Take the Mum Off You When You Come In': An Ethnographic Study of Parent Experiences of Medicines Safety Systems in English Hospitals.Health expectations : an international journal of public participation in health care and health policy · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Natalie SanfordThe Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College, James Clerk Maxwell Building 1.32, 57 Waterloo Road, London, SE1 8WA, UK. Natalie.Sanford@kcl.ac.uk.
Mary LavelleSchool of Psychology, Queen's University Belfast, Belfast, UK.
Ola MarkiewiczNIHR Patient Safety and Translational Research Centre, Imperial College London, London, UK.
Gabriel ReedyCentre for Education, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Anne Marie RaffertyThe Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College, James Clerk Maxwell Building 1.32, 57 Waterloo Road, London, SE1 8WA, UK.
Ara DarziNIHR Patient Safety and Translational Research Centre, Imperial College London, London, UK.
Janet E AndersonFaculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResilient Healthcare research centres on understanding and improving quality and safety in healthcare. The Concepts for Applying Resilience Engineering (CARE) model highlights the relationships between demand, capacity, work-as-done, work-as-imagined, and outcomes, all of which are central aspects of Resilient Healthcare theory. However, detailed descriptions of the nature of misalignments and the mechanisms used to adapt to them are still unknown.

objectiveThe objectives were to identify and classify types of misalignments between demand and capacity and types of adaptations that were made in response to misalignments.

methodsThe study involved 88.5 hours of non-participant ethnographic observations in a large, teaching hospital in central London. The wards included in the study were: two surgical wards, an older adult ward, a critical care unit, and the Acute Assessment Unit (AAU), an extension unit created to expedite patient flow out of the Emergency Department. Data were collected via observations of routine clinical work and ethnographic interviews with healthcare professionals during the observations. Field notes were transcribed and thematically analysed using a combined deductive-inductive approach based on the CARE model.

resultsA total of 365 instances of demand-capacity misalignment were identified across the five wards included in the study. Of these, 212 had at least one observed corresponding work adaptation. Misalignments identified include equipment, staffing, process, communication, workflow, and space. Adaptations identified include process, resource redistribution, and extra-role performance. For all misalignment types observed across the five in-patient settings, process adaptations were the most frequently used adaptations. The exception to this was for staffing misalignments, which were most frequently responded to with extra-role performance adaptations. Of the three process adaptations, hospital workers most often adapted by changing how the process was done.

conclusionsThis study contributes a new version of the CARE model that includes types of misalignments and corresponding adaptations, which can be used to better understand work-as-done. This affords insight into the complexity of the system and how it might be improved by reducing misalignments via work system redesign or by enhancing adaptive capacity.

Indexed as

CommunicationDelivery of Health CareAgedEmergency Service, HospitalHospitalsHumansPersonnel, HospitalAdaptive capacityCARE modelComplex systemsResilient healthcareWork-as-done

Identifiers

PMID36068564
PMCPMC9450258

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.