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Record W3216422894 · doi:10.1002/acr.24822

Implementation of Rheumatology Health Care Transition Processes and Adaptations to Systems Under Stress: A <scp>Mixed‐Methods</scp> Study

2021· article· en· W3216422894 on OpenAlexaff
Joyce C. Chang, Cora Sears, Nicole Bitencourt, Rosemary Peterson, Risa Alperin, Y. Ingrid Goh, Rebecca S. Overbury, Rebecca E. Sadun, Emily A. Smitherman, Patience H. White, Erica Lawson, Kristine Carandang

Bibliographic record

VenueArthritis Care & Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsSickKids Foundation
FundersChildhood Arthritis and Rheumatology Research AllianceArthritis Foundation
KeywordsPsychological interventionPsychosocialNursingTransitional careFacilitatorMedicineHealth careMedical educationPsychologyPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: Despite poor health care transition outcomes among young adults with pediatric rheumatic diseases, adoption of transition best practices is low. We sought to understand how structured transition processes were operationalized within pediatric rheumatology practices and what factors were perceived to enable adaptations during a global pandemic. METHODS: We conducted a mixed methods study of team leaders' experiences during an interim analysis of a pilot project to implement transition policy discussions at sites in the Childhood Arthritis and Rheumatology Research Alliance Transition Learning Collaborative. We combined quantitative assessments of organizational readiness for change (9 sites) and semistructured interviews of team leaders (8 sites) using determinants in the Exploration, Preparation, Implementation, Sustainment Framework. RESULTS: Engagement of nursing and institutional improvement efforts facilitated decisions to implement transition policies. Workflows incorporating educational processes by nonphysicians were perceived to be critical for success. When the pandemic disrupted contact with nonphysicians, capacity for automation using electronic medical record (EMR)-based tools was an important facilitator, but few sites could access these tools. Sites without EMR-based tools did not progress despite reporting high organizational readiness to implement change at the clinic level. Lastly, educational processes were often superseded by acute issues, such that youth with greater medical/psychosocial complexity may not receive the intervention. CONCLUSION: We generated several considerations to guide implementation of transition processes within pediatric rheumatology from the perspectives of team leaders. Careful assessment of institutional and nursing support is advisable before conducting complex transition interventions. Ideally, new strategies would ensure interventions reach youth with high complexity.

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)0.042
metaresearch head score (Gemma)0.033
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 score0.042
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0020.002
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 head0.138
GPT teacher head0.555
Teacher spread0.417 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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