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Record W2099866116 · doi:10.1186/s13012-014-0161-5

Data for improvement and clinical excellence: report of an interrupted time series trial of feedback in long-term care

2014· article· en· W2099866116 on OpenAlex
Anne Sales, Corinne Schalm, Melba Andrea B. Baylon, Kimberly D. Fraser

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueImplementation Science · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of AlbertaAlberta Health
FundersFondation pour la Recherche MédicaleUniversity of AlbertaCanadian Health Services Research Foundation
KeywordsMedicineInterrupted Time Series AnalysisIntervention (counseling)Psychological interventionLong-term careMinimum Data SetInterrupted time seriesRandomized controlled trialAuditHealth services researchHealth administrationExcellencePhysical therapyEmergency medicineFamily medicinePublic healthNursingSurgeryNursing homes

Abstract

fetched live from OpenAlex

BACKGROUND: There is substantial evidence about the effectiveness of audit with feedback, but none that we know have been conducted in home care settings. The primary purpose of the Data for Improvement and Clinical Excellence - Home Care (DICE-HC) project was to evaluate the effects of an audit and feedback delivered to care providers on home care client outcomes. The objective of this paper is to report the effects of feedback on four specific quality indicators: pain, falls, delirium, and hospital visits. METHODS: A 10-month audit with feedback intervention study was conducted with care providers in seven home care offices in Alberta, Canada, which involved delivery of four quarterly feedback reports consisting of data derived from the Resident Assessment Instrument - Home Care (RAI-HC). The primary evaluation employed an interrupted time series design using segmented regression analysis to assess the effects of feedback reporting on the four quality indicators: pain, falls, delirium, and hospitalization. Changes in level and trend of the quality indicators were measured before, during, and after the implementation of feedback reports. Pressure ulcer reporting was analyzed as a comparator condition not included in the feedback report. Care providers were surveyed on responses to feedback reporting which informed a process evaluation. RESULTS: At initiation of feedback report implementation, the percentage of clients reporting pain and falls significantly increased. Though the percentage of clients reporting pain and falls tended to increase and reporting of delirium and hospital visits tended to decrease relative to the pre-intervention period, there was no significant effect of feedback reporting on quality indicators during the 10-month intervention. The percentage of clients reporting falls, delirium, and hospital visits significantly increased in the 6-month period following feedback reporting relative to the intervention period. About 50% of the care providers that read and understand the feedback reports found the reports useful to make changes to the way clients are cared for. CONCLUSIONS: Routinely collected data used over time for feedback is feasible in home care settings. A high proportion of care providers find feedback reports useful for informing how they care for clients. Since reporting on the frequency of quality indicators increased in the post-intervention period, this study suggests that ongoing use of audit with feedback to enhance health outcomes in home care may promote improved reporting on standardized instruments.

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.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.167
GPT teacher head0.580
Teacher spread0.413 · 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