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Hemoglobinopathy Education in Canadian Hematology Training Programs: How Much Are Residents Learning?

2014· article· en· W2979832351 on OpenAlexaffabout
Madeleine Verhovsek, Vicky R. Breakey, Richard Ward, Shannon M. Bates, Parveen Wasi, Nancy Robitaille

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversity of TorontoMcMaster University
Fundersnot available
KeywordsHemoglobinopathyMedicineThalassemiaPublic healthFamily medicineDiseasePediatricsInternal medicineNursing

Abstract

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Abstract The number of hemoglobinopathy patients in North America continues to increase, due to high rates of immigration from countries with high prevalence and improved survival. Recent research has led to evidence-based improvements in acute and chronic care of patients with sickle cell disease and thalassemia. Studies have noted gaps in clinicians’ knowledge about management of hemoglobinopathies, with the result that common presentations, such as sickle vaso-occlusive episodes, are often mismanaged. Hematologists completing training in North America require the knowledge and expertise to manage these medically complex patients. To ascertain the extent of hemoglobinopathy teaching and exposure in Canadian Adult and Pediatric Hematology Training Programs, and to assess the perceived importance of hemoglobinopathy knowledge, we administered an online survey to all Training Program Directors (TPDs), and to all residents who were currently enrolled or who had completed training in the previous year. Surveys were available in English and French. The response rate for TPDs was 92% (22/24). Ninety five percent of PDs felt that hemoglobinopathy learning is “important” or “very important” for hematology trainees in their region. Four programs have a mandatory hemoglobinopathy rotation, 14 programs have mandatory hemoglobinopathy clinic participation, and 17 programs have mandatory hemoglobinopathy lab exposure. Laboratory time ranges widely, from “0-2 hours” to “greater than 20 hours”. All programs covered laboratory aspects of hemoglobinopathy, outpatient care of sickle cell disease and inpatient care of sickle cell disease, and all but one program covered outpatient care of thalassemias. In 1/2 to 2/3 of adult training programs, these topics were covered at only a basic level. All pediatric programs covered outpatient and inpatient care of sickle cell disease “in-depth”, with 90% and 40% of programs covering outpatient thalassemia care and laboratory diagnostics “in-depth”, respectively. All 22 programs had academic half-days with teaching devoted to hemoglobinopathy. Seventy-seven percent of programs had faculty member(s) with an interest in hemoglobinopathy. The response rate for residents was 45% (70/156). The majority of respondents were senior residents, with 88% currently in post-graduate year five, or above. Among residents in adult hematology programs, 61% had completed a rotation or elective with a focus on hemoglobinopathy versus 25% in the pediatric programs. Total numbers of hemoglobinopathy patients seen ranged from “0” to “more than 50”, and laboratory exposure varied from “none” to “in-depth”. Most residents with clinical hemoglobinopathy experience had seen patients with both sickle cell disease and thalassemia major or intermedia. Of residents who responded, 83% felt that hemoglobinopathy knowledge was “important” or “very important” to their future practice. All TPDs and 90% of residents felt that online hemoglobinopathy learning modules could be beneficial for resident learning. There appears to be wide variability in depth and breadth of clinical and laboratory hemoglobinopathy learning in Hematology training programs across Canada. TPDs and residents place high importance on hemoglobinopathy learning, but some centres have small patient numbers, or lack faculty with interest in hemoglobinoapthies. Online learning modules could provide added learning opportunities for residents. Disclosures No relevant conflicts of interest to declare.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.239
Teacher spread0.230 · 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.

Study designObservational
DomainEvaluation
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
Published2014
Admission routes2
Has abstractyes

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