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Record W2734904481 · doi:10.5334/ijic.3251

The Unmet Needs of Patients and Carers within Community Based Primary Health Care

2017· article· en· W2734904481 on OpenAlexaffabout
Kerry Kuluski, Ashlinder Gill, Ann McKillop, John Parsons, Allie Peckham, Nicolette Sheridan, Ross Upshur, Cecilia Wong-Cornall

Bibliographic record

VenueInternational Journal of Integrated Care · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrimary careNursingMedicinePrimary health careIntegrated careFamily medicineHealth carePopulationEnvironmental health

Abstract

fetched live from OpenAlex

Introduction: Community Based Primary Health Care (CBPHC) is positioned as the foundation of integrated health systems, intended to support broader goals of population health and health system sustainability. CBPHC moves beyond traditional primary care (a physician visit) to team based care that spans organizational boundaries (such as primary care clinics + community care services). At the core of CBPHC are patients and their informal carers (family and friends) who can inform ongoing reforms in this sector by sharing their experience, particularly in areas that require improvement. The objective of this paper is to share the unmet needs of patients and caregivers within CBPHC.Methods: This study is part of a broader programme of research called, implementing integrated care for older adults with complex health needs (iCOACH). Semi-structured interviews are being conducted with older patients with complex care needs and with unpaid, informal carers across multiple CBPHC sites in Canada (Ontario and Quebec) and New Zealand. Interviews captured the roles, characteristics and needs of patients and carers, and were audio-recorded and transcribed verbatim. Interviews were reviewed by multiple team members and a consensus codebook was created. The code “unmet need” was extracted from the patient and carer transcripts, and analyzed for core themes using an inductive approach.Results: Unmet needs culminated into three broad themes across patient and carer interviews: Accessing Care; Quality of Care; and Missing Care. Many patients accessed care within CBPHC, but the model itself tended to be spread across multiple settings and providers. Patients and carers also required access to services that were outside the CBPHC model. Challenges arose due to lack of transportation, out-of-pocket expenses, limited availability of assistive devices to support mobility, and long wait times. Quality of care and relationships were compromised if there was a language barrier, and when services were misaligned with the preferences of patients and carers. Components of care were often missing, such as respite care for carers, supports for instrumental activities of daily living (e.g., home maintenance and transportation), and supports to reduce social isolation.Conclusions: Due to the complex health and social needs of patients and carers, they often require access to multiple services and providers who are seldom situated under the same roof . Finding ways to integrate across organizational boundaries may reduce areas of unmet need. Furthermore greater attention to the social determinants of health within CBPHC may create a more holistic experience for patients and carers.Lessons Learned: CBPHC is intended to deliver holistic integrated care but when situated within a fragmented health care system, challenges for patients and carers persist.Limitations: Further comparisons need to be made between the unmet needs between patients and caregivers in Canada and New Zealand.Suggestions for future research: Next steps will include the development of a framework that describes the policy and organizational context, and provider configurations in each of the study jurisdictions. How these factors relate to different patient and carer needs and experiences, including areas of unmet need, will be explored to inform the development of person centered CBPHC models.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.298
Teacher spread0.261 · 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 designObservational
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

Citations3
Published2017
Admission routes2
Has abstractyes

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