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Record W2795082455 · doi:10.1071/hc17074

Effect of multimorbidity on health service utilisation and health care experiences

2018· article· en· W2795082455 on OpenAlexaff
Elinor Millar, James Stanley, Jason Gurney, Jeannine Stairmand, Cheryl Davies, Kelly Semper, Anthony Dowell, Ross Lawrenson, Dee Mangin, Diana Sarfati

Bibliographic record

VenueJournal of Primary Health Care · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMultimorbidityMedicineHealth careNursingPrimary careFamily medicinePopulationEthnic groupPopulation healthMedical homePublic healthEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION Multimorbidity, the co-existence of two or more long-term conditions, is associated with poor quality of life, high health care costs and contributes to ethnic health inequality in New Zealand (NZ). Health care delivery remains largely focused on management of single diseases, creating major challenges for patients and clinicians. AIM To understand the experiences of people with multimorbidity in the NZ health care system. METHODS A questionnaire was sent to 758 people with multimorbidity from two primary health care organisations (PHOs). Outcomes were compared to general population estimates from the NZ Health Survey. RESULTS Participants (n = 234, 31% response rate) reported that their general practitioners (GPs) respected their opinions, involved them in decision-making and knew their medical history well. The main barriers to effective care were short GP appointments, availability and affordability of primary and secondary health care, and poor communication between clinicians. Access issues were higher than for the general population. DISCUSSION Participants generally had very positive opinions of primary care and their GP, but encountered structural issues with the health system that created barriers to effective care. These results support the value of ongoing changes to primary care models, with a focus on patient-centred care to address access and care coordination.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.044
GPT teacher head0.406
Teacher spread0.362 · 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 designOther design
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

Citations21
Published2018
Admission routes1
Has abstractyes

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