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Record W3009913461 · doi:10.1186/s43058-020-00013-9

Effectiveness of confidential reports to physicians on their prescribing of antipsychotic medications in nursing homes

2020· article· en· W3009913461 on OpenAlex

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 Communications · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsSunnybrook HospitalSt. Michael's HospitalOttawa HospitalUniversity of OttawaInstitute for Clinical Evaluative SciencesWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsMedicineAntipsychoticFamily medicinePopulationIntervention (counseling)NursingAuditPsychiatrySchizophrenia (object-oriented programming)Environmental health

Abstract

fetched live from OpenAlex

Abstract Background Antipsychotic medication use in nursing homes is associated with potential for harms. In Ontario, Canada, an agency of the provincial government offers nursing home physicians quarterly audit and feedback on their antipsychotic prescribing. We compared the characteristics of physicians who did and did not engage with the intervention, and assessed early changes in prescribing. Methods This population-level, retrospective cohort study used linked administrative databases to track prescribing practices in nursing homes pre-intervention (baseline), immediately post-initiative (3 months), and at follow-up (6 months). Exposure variables identified whether a physician signed up to participate (or not) or viewed the feedback following sign up (or not). Differences in the proportion of days that residents received antipsychotic medications at 6 months compared to baseline by exposure(s) were assessed using a linear mixed effects regression analysis to adjust for a range of resident, physician, and nursing home factors. Benzodiazepine and statin prescribing were assessed as a balance and tracer measures, respectively. Results Of 944 eligible physicians, 210 (22.3%) signed up to recieve the feedback report and 132 (13.9%) viewed their feedback. Physicians who signed up for feedback were more likely to have graduated from a Canadian medical school, work in urban nursing homes, and care for a larger number of residents. The clinical and functional characteristics of residents were similar across physician exposure groups. At 6 months, antipsychotic prescribing had decreased in all exposure groups. Those who viewed their feedback report had a signicantly greater reduction in antipsychotic prescribing than those who did not sign up (0.94% patient-days exposed; 95% CI 0.35 to 1.54%, p = 0.002). Trends in prescribing patterns across exposure groups for benzodiazepines and statins were not statistically significant. Interpretation Almost a quarter of eligible physicians engaged early in a voluntary audit and feedback intervention related to antipsychotic prescribing in nursing homes. Those who viewed their feedback achieved a small but statistically significant change in prescribing, equivalent to approximately 14,000 fewer days that nursing home residents received antipsychotic medications over 6 months. This study adds to the literature regarding the role of audit and feedback interventions to improve quality of care.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.091
GPT teacher head0.490
Teacher spread0.399 · 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