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Record W4309651054 · doi:10.1186/s43058-022-00369-0

Reporting unit context data to stakeholders in long-term care: a practical approach

2022· article· en· W4309651054 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 · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of CalgaryUniversity of ManitobaUniversity of TorontoDalhousie UniversityUniversity of AlbertaYork University
FundersCanadian Institutes of Health Research
KeywordsTerm (time)Context (archaeology)Unit (ring theory)Long-term careComputer scienceProcess managementBusinessMedicinePsychologyNursingHistory

Abstract

fetched live from OpenAlex

BACKGROUND: The importance of reporting research evidence to stakeholders in ways that balance complexity and usability is well-documented. However, guidance for how to accomplish this is less clear. We describe a method of developing and visualising dimension-specific scores for organisational context (context rank method). We explore perspectives of leaders in long-term care nursing homes (NHs) on two methods for reporting organisational context data: context rank method and our traditionally presented binary method-more/less favourable context. METHODS: We used a multimethod design. First, we used survey data from 4065 healthcare aides on 290 care units from 91 NHs to calculate quartiles for each of the 10 Alberta Context Tool (ACT) dimension scores, aggregated at the care unit level based on the overall sample distribution of these scores. This ordinal variable was then summed across ACT scores. Context rank scores were assessed for associations with outcomes for NH staff and for quality of care (healthcare aides' instrumental and conceptual research use, job satisfaction, rushed care, care left undone) using regression analyses. Second, we used a qualitative descriptive approach to elicit NH leaders' perspectives on whether the methods were understandable, meaningful, relevant, and useful. With 16 leaders, we conducted focus groups between December 2017 and June 2018: one in Nova Scotia, one in Prince Edward Island, and one in Ontario, Canada. Data were analysed using content analysis. RESULTS: Composite scores generated using the context rank method had positive associations with healthcare aides' instrumental research use (p < .0067) and conceptual research use and job satisfaction (p < .0001). Associations were negative between context rank summary scores and rushed care and care left undone (p < .0001). Overall, leaders indicated that data presented by both methods had value. They liked the binary method as a starting point but appreciated the greater level of detail in the context rank method. CONCLUSIONS: We recommend careful selection of either the binary or context rank method based on purpose and audience. If a simple, high-level overview is the goal, the binary method has value. If improvement is the goal, the context rank method will give leaders more actionable details.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0000.001
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 head0.718
GPT teacher head0.643
Teacher spread0.074 · 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