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Record W2235648668 · doi:10.1177/1049732315619893

The Influence of Organizational Systems on Information Exchange in Long-Term Care Facilities

2016· article· en· W2235648668 on OpenAlexaff
Sienna Caspar, Pamela A. Ratner, Alison Phinney, Karen MacKinnon

Bibliographic record

VenueQualitative Health Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsToronto Rehabilitation InstituteUniversity of VictoriaUniversity of British ColumbiaUniversity Health Network
Fundersnot available
KeywordsTerm (time)NursingLong-term carePsychologyInformation systemBusinessKnowledge managementMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Person-centered care is heavily dependent on effective information exchange among health care team members. We explored the organizational systems that influence resident care attendants' (RCAs) access to care information in long-term care (LTC) settings. We conducted an institutional ethnography in three LTC facilities. Investigative methods included naturalistic observations, in-depth interviews, and textual analysis. Practical access to texts containing individualized care-related information (e.g., care plans) was dependent on job classification. Regulated health care professionals accessed these texts daily. RCAs lacked practical access to these texts and primarily received and shared information orally. Microsystems of care, based on information exchange formats, emerged. Organizational systems mandated written exchange of information and did not formally support an oral exchange. Thus, oral information exchanges were largely dependent on the quality of workplace relationships. Formal systems are needed to support structured oral information exchange within and between the microsystems of care found in LTC.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.236
GPT teacher head0.570
Teacher spread0.334 · 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 designQualitative
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

Citations44
Published2016
Admission routes1
Has abstractyes

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