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Record W7162040403 · doi:10.82308/8187

Improving chronic illness care: Implementation and evaluation of interdisciplinary and patient-centred care

2019· dissertation· en· W7162040403 on OpenAlexaboutno aff
Amédé Gogovor

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Health careChronic carePublic healthQualitative researchMultiple Chronic ConditionsChronic diseaseMEDLINEQuality (philosophy)

Abstract

fetched live from OpenAlex

The prevalence of chronic diseases and conditions is steadily increasing in Canada and globally. Despite the availability of effective therapies, the management of chronic diseases remains far from optimal. Several reports and practice guidelines recommend that care should be patient-centred and delivered by health care teams as means to empower patients to engage in their own care decisions, enhance coordination of care, and make more efficient use of resources, ultimately leading to improved patient outcomes and safety. However, many challenges remain in implementing these approaches, including assessing and accumulating evidence on their effectiveness. The overall aim of this thesis is to contribute to evidence-informed patient-centred and interdisciplinary team (IDT) care in the context of quality chronic illness care through two interrelated research projects. Using a population-based survey, the first project assessed the level of support for patient-centred care (PCC) amongst the Canadian public and among health professionals. Significant associations were identified between support for PCC and support for both team-based care and the use of health information technology. These associations were identified from both the public and health professionals' perspectives. The second project used a convergent mixed methods design to investigate experiences of primary interdisciplinary care for low back pain. For the qualitative component, I employed a phenomenological approach to better understand the delivery and perceived impact of IDT care. The quantitative component used the Patient Assessment of Chronic Illness Care (PACIC) questionnaire to evaluate change in patient experience and to estimate the impact of patient and process variables on patient experience. The findings from the two components were reviewed for convergence, complementarity and discrepancy. Findings from project 1 suggest that implementation of health care teams supported by information and communication technologies are needed to deliver PCC. From the perspective of the participants in the qualitative inquiry of project 2, IDT care contributed to effective and patient-centred primary care. The quantitative component showed improved experience of care for the majority of the participants but did not demonstrate significant associations between change in experience of care and patient and process outcomes. Overall, implementing an IDT appears to be an appropriate approach to deliver PCC and improve the quality of chronic illness care. Based on these analyses, I propose strategies to help improve the implementation of IDT programs for low back pain. These recommendations can also inform similar primary care programs for other chronic conditions. Directions for future research include further evaluation of the structure and construct validity of the PACIC, and continued investigation of the relationships between PCC, patient experience, patient factors, and outcomes.

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)0.155
metaresearch head score (Gemma)0.174
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.155
Threshold uncertainty score0.821

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1550.174
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.004
Science and technology studies0.0040.004
Scholarly communication0.0070.005
Open science0.0040.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.464
Teacher spread0.440 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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