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Record W3033180632 · doi:10.1136/bmjoq-2019-000809

Checklists in community care: reducing differences in care delivery between regular and relief staff to improve consistency and client experience

2020· article· en· W3033180632 on OpenAlexaff
Swapnil Rege, Aisha Mian Malik, Marybeth Ward, Jing Hong

Bibliographic record

VenueBMJ Open Quality · 2020
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsRegional Municipality of Ottawa
Fundersnot available
KeywordsChecklistService delivery frameworkNursingPsychological interventionMedicineAgency (philosophy)Consistency (knowledge bases)Service (business)BusinessPsychologyMarketingComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Today, healthcare is more complex than just ensuring clients receive quality care; it also involves consistently delivering excellent client experience. A non-profit community support services agency conducted an extensive diagnostic journey to determine root causes of inconsistent care delivery between regular and relief frontline staff. LOCAL PROBLEM: Clients and family caregivers noted lower satisfaction in care delivery when a relief staff (ie, internal staff or an external agency that is covering a shift) provided service in comparison with their regular staff. The diagnostic journey discovered that the shift exchange process-when outgoing staff transfers critical knowledge to incoming staff for continuing care-varied significantly between the 11 service locations, leading to a lack of consistent service delivery, thereby impacting client experience. METHODS: A working group consisting of Supervisors of Client Services, Personal Support Workers (PSW) and management were tasked with process mapping the current state, highlighting gaps and outlining the ideal state of the shift exchange process. INTERVENTIONS: Using best practices from the aviation industry, a checklist was developed that encapsulated all the critical steps needed to be undertaken for a successful, consistent shift exchange. The theory was that the utilisation of the checklist would enable consistency and improve client satisfaction with care delivery, especially when care is delivered by a staff unfamiliar with clients. RESULTS: Prior to the checklist implementation, 74% of clients were satisfied or very satisfied with their relief staff, and post checklist implementation client satisfaction improved to 90%. Staff self-assessments also indicated that PSWs agreed that the checklist helped provide consistent care. CONCLUSION: The use of checklists can transform the way care is delivered in the community support sector and other service delivery agencies alike to bring greater standardisation of care between providers, thus significantly improving client experience across the healthcare sector.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.411
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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