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Record W1967220401 · doi:10.4236/health.2012.429113

Understanding organizational context and heart failure management in long term care homes in Ontario, Canada

2012· article· en· W1967220401 on OpenAlexafffundabout
Jill Marcella, Jayanthini Nadarajah, Mary Lou Kelley, George Heckman, Sharon Kaasalainen, Patricia H. Strachan, Robert S. McKelvie, Ian Newhouse, Paul Stolee, Carrie McAiney, Catherine Demers

Bibliographic record

VenueHealth · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsHamilton Health SciencesPopulation Health Research InstituteMcMaster UniversityUniversity of WaterlooNOSM UniversityUniversity of OttawaLakehead University
FundersHeart and Stroke Foundation of Canada
KeywordsLong-term careContext (archaeology)Flexibility (engineering)NursingGovernment (linguistics)MedicineFocus groupWork (physics)BusinessMarketingManagement

Abstract

fetched live from OpenAlex

Objective: To assess current heart failure (HF) care processes and organizational context in long-term care (LTC) homes as a prelude to adapting the Canadian Cardiovascular Society (CCS) HF guidelines for use in these settings. Methods: This research reports on the results of thirteen focus groups (N = 83 participants; average of 60 minutes duration) conducted in three Ontario LTC homes to better understand how HF was managed and how organizational context impacted care. Participants included physicians, nurse practitioners, registered nurses, registered practical nurses, and personal support workers. Results: Focus group findings revealed that the complexity of the LTC environment presents challenges for managing HF. Most residents have multiple advanced chronic conditions that must be managed simultaneously. Culturally, LTC is first and foremost a resident’s home where residents may choose not to comply with care recommendations. Staff routines, scopes of practice, professional hierarchies, available resources and government regulations limit flexibility in providing care. Staff lacked knowledge, skills and resources for managing HF. Nevertheless, all staff viewed LTC as the preferred place for managing HF, avoiding residents’ hospitalizations wherever possible. These data suggest that strategies for improving LTC staff communication and education, strengthening existing relationships between staff, family, residents and community resources, and acquiring additional resources in LTC homes have the potential to improve HF management in this setting. Conclusion: LTC is a complex and dynamic environment that presents many challenges for providing care for residents. This research provides the foundation for subsequent work to develop and test implementation strategies to manage HF in LTC, which are consistent with the CCS HF guidelines and are feasible within LTC staff’s work routines, capacities and resources.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.721

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.338
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2012
Admission routes3
Has abstractyes

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