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Record W4323351262 · doi:10.1093/jcag/gwac036.090

A90 IMPLEMENTATION OF NATIONAL GUIDELINES ON THE MANAGEMENT OF VACCINE PREVENTABLE ILLNESS IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE: PERCEIVED BARRIERS AND INTERVENTION FUNCTIONS AMONGST GASTROENTEROLOGISTS

2023· article· en· W4323351262 on OpenAlexaff
Felix Zhou, James L. Robar, M Stewart, J. B. Jones

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsMedicineThematic analysisPsychological interventionFamily medicineVaccinationIntervention (counseling)DiseaseCoding (social sciences)Qualitative researchNursingInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Background Vaccination uptake amongst patients with IBD remains suboptimal. Studies evaluating effectiveness of interventions designed to improve vaccine uptake have not assessed perceived barriers and solutions related to implementation of evidence-based guidelines for vaccine preventable illness (VPI). Purpose The aim of this study was to identify barriers and facilitators for evidence-based management of VPI in IBD. Method A semi-structured interview was conducted with gastroenterologists. Interview questions were developed and guided by the COM-B and TDF evidence-based implementation science frameworks. A brief intake questionnaire was administered to collect participant demographic and clinical practice information. Gastroenterologists were recruited through direct local contact via email by the investigators. Sixty minute interviews were recorded and transcribed for data analysis. Using thematic analysis, codes from the study data will be generated to identify themes. The data will be categorized into the coding scheme and themes created using an inductive coding approach. Result(s) As of October 2022, 5 interviews were conducted. Mean participant age was 47.8 years, with 60% identifying practice in an urban/academic setting compared to a rural/community setting (20%). Preliminary major themes included 1) assessing vaccination status and recommending appropriate vaccines are the responsibility of the gastroenterologist 2) gastroenterologists need more support to administer vaccines in clinical practice 3) barriers to implementation of VPI guidelines include lack of access to a family physician, limited time, vaccine hesitancy, and incomplete understanding of coverage/access to vaccines and 4) intervention themes include use of clinical decision support tools embedded into the workflow of healthcare providers, need for support from allied healthcare providers, increased need for third party support, and more education/CME relating to management of VPI in clinical practice. Specific knowledge gaps include 1) uncertainty relating to what vaccines are covered financially 2) lack of knowledge of risk factors for specific VPI such as pneumococcus and meningococcus and 3) how to administer live vaccines in patients already on immunosuppressants. Conclusion(s) Preliminary qualitative themes suggest that although gastroenterologists acknowledge the importance of managing VPI in patients with IBD, perceived resource, policy, and educational barriers exist. The qualitative data from this study will be used to design and implement customized, evidence-based implementation strategies for managing VPI that are sensitive to the local environment. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

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)0.016
metaresearch head score (Gemma)0.067
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 score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.067
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.260
Teacher spread0.251 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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