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Record W7062529440

Understanding and measuring clinician-patient relationship qualities in the setting of serious illness to promote healthcare quality and equity: A manuscript-based thesis

2024· dissertation· en· W7062529440 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2024
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsQuality (philosophy)Health careContext (archaeology)Qualitative researchQuality of life (healthcare)MEDLINE
DOInot available

Abstract

fetched live from OpenAlex

Background: Serious illnesses pose many challenges to patients and their caregivers.They contribute to multiple forms of distress: physical, psychological, social, and spiritual.Relationships, including those between clinicians and patients, have the potential to mitigate patient suffering and promote experiences of growth and integrity through the illness journey and up to and beyond the end of life.Indeed, they are critical to processes of meaning making and the maintenance of these relationships is often the focus of patients' goals.Threats to the quality of clinical relationships are many and include systemic factors, such as poor healthcare access or clinician time, and interpersonal factors, such as language concordance and implicit bias.Despite their importance, we know little about the experience of clinician-patient relationships in the setting of potential interpersonal challenges, and little about how to measure relationship quality as a primary outcome of research and care.Objectives: (1) To examine the scope of current literature on valid survey instruments that assess the quality of relationships between people affected by serious illness and clinicians; and (2) To explore the qualities of healing clinician-patient relationships from the perspective of patients from visible minority groups who have received palliative care in Canada. Methods:In the first manuscript, we conducted a scoping review based on Arksey and O'Malley's (2005) framework and PRISMA-ScR guidelines.We included studies from CINAHL, Embase, and PubMed that describe the validation of survey instruments assessing care experiences of patient populations affected by serious illness.We categorized relationshipfocused items based on empirically derived domains of relationship quality.In the second Measuring the quality of patient-provider relationships in serious illness

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)0.017
metaresearch head score (Gemma)0.108
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 score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.108
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.159
GPT teacher head0.340
Teacher spread0.181 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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