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Record W3120748376 · doi:10.21203/rs.3.rs-136225/v1

Readiness to Implement Symptom Management Care Pathways in Pediatric Cancer

2020· preprint· en· W3120748376 on OpenAlexafffund
L. Lee Dupuis, Allison Grimes, Emily Vettese, Lisa M. Klesges, Lillian Sung

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsImplementation researchCare pathwayMedicineClinical pathwayFamily medicineHealth careNursingPsychological interventionPolitical science

Abstract

fetched live from OpenAlex

Abstract Background: To describe barriers to pediatric cancer symptom management care pathway implementation and the impact of the COVID-19 pandemic on clinical research evaluating their implementation. Methods: We included 25 pediatric oncology hospitals in the United States that supported a grant submission to perform a cluster randomized trial in which the intervention encompassed care pathways for symptom management. A survey was distributed to site principal investigators to measure contextual elements related to care pathway implementation. Questions included the inner setting measures of the Consolidated Framework for Implementation Research (CFIR), study-specific potential barriers and the impact of the COVID-19 pandemic on clinical research. The Wilcoxon rank sum test was used to compare characteristics of institutions that agreed that their department supported the implementation of symptom management care pathways vs. institutions that did not agree. Results: Of the 25 sites, one withdrew because of resource constraints and one did not respond, leaving 23 institutions. Among the seven CFIR constructs, the least supported was implementation climate; 57% agreed there was support, 39% agreed there was recognition and 39% agreed there was prioritization for symptom management care pathway implementation at their institution. Most common barriers were lack of person-time to create care pathways and champion their use (35%), lack of interest from physicians (30%) and lack of information technology resources (26%). Most sites reported no negative impact of the COVID-19 pandemic across research activities. Sites with fewer pediatric cancer patients were more likely to agree that staff are supported to implement symptom management care pathways (P=0.003). Conclusions: The most commonly reported barriers to implementation were lack of support, recognition and prioritization. The COVID-19 pandemic was not a major barrier to clinical research activities in pediatric oncology. Clinical Trial Registration: NCT04614662

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)0.016
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.008
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0030.012
Research integrity0.0010.011
Insufficient payload (model declined to judge)0.0010.001

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 head0.189
GPT teacher head0.493
Teacher spread0.305 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2020
Admission routes2
Has abstractyes

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