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Record W2099765717 · doi:10.12968/bjon.2015.24.7.394

Examining the effect of patient-centred care on outcomes

2015· article· en· W2099765717 on OpenAlexaff
Suzanne Fredericks, Jennifer Lapum, Gladys Hui

Bibliographic record

VenueBritish Journal of Nursing · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionMedicineIntervention (counseling)Health careSystematic reviewDescriptive statisticsMEDLINENursingFamily medicine

Abstract

fetched live from OpenAlex

Within patient-centered care (PCC), the individual is viewed as an active member of the healthcare team. While there has been recent interest in conducting systematic reviews to examine the effectiveness of PCC interventions, various studies fall short in explaining the type of intervention most effective in producing significant changes to desired outcomes. The purpose of this systematic review was to determine the characteristics of PCC interventions that have demonstrated effectiveness in enhancing the quality of care and performance of self-care behaviours. A systematic review of 40 studies that addressed PCC interventions, included samples over the age of 18 years, and were published between 1995 and 2014 was performed. Descriptive statistics were used to delineate study, participant, and intervention characteristics. Results suggest PCC-based interventions are not effective when delivered to individuals living with chronic illnesses.

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)0.046
metaresearch head score (Gemma)0.167
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: none
Teacher disagreement score0.046
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.167
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.014
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.142
GPT teacher head0.431
Teacher spread0.289 · 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

Citations21
Published2015
Admission routes1
Has abstractyes

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