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Record W4385655472 · doi:10.1002/jha2.760

Empowering patients with sickle cell anemia and their families through innovative educational methods

2023· article· en· W4385655472 on OpenAlexaffabout
Riley Plett, Craig Eling, Sarah Tehseen, Kathleen Felton, Gina Martin, Vivian Sheppard, Megan Pegg, Roona Sinha

Bibliographic record

VenueeJHaem · 2023
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineFocus groupThematic analysisFamily medicineSickle cell anemiaDiseasePediatricsPsychologyQualitative researchInternal medicine

Abstract

fetched live from OpenAlex

Abstract Sickle cell disease (SCD) is a group of inherited blood disorders caused by a mutation in the beta subunit of hemoglobin (HbS). SCD will hereafter be referred to as sickle cell anemia (SCA) as this is the term our patients and their families prefer. There are approximately 5000 Canadians living with SCA including children. Pediatric SCA patient education can: improve knowledge, decrease hospitalization, improve medication possession ratio, lead to better SCA‐related functioning, and lower pain impact. Innovative educational materials were developed to improve knowledge and self‐efficacy regarding the illness management of patients and parents/guardians. Patients (n = 5; aged 8–18) with SCA and parents (n = 5) of patients (aged 0–18) were recruited via flyers sent directly to patients and distributed through partner patient organization Sickle Cell Awareness Network of Saskatchewan. Patient and parent focus groups were held separately over Zoom to receive feedback for the video. An additional interview was held for a participant that required a translation of the video. Audio recordings were transcribed using Zoom and Otter.ai. The coding of transcripts was facilitated by NVivo (QSR International Pty Ltd, 2022, release 1.6.2). The thematic analysis centered around SCA management concepts relevant to the research aims. Important themes that emerged included ‘Age Appropriateness’, ‘Empowerment’, ‘Knowledge Gaps’, ‘Linguistic Accessibility’, ‘Medication Adherence’, ‘Strength in Community’, and ‘Transition to Adult Care’. The video was well received, and “brought peace of mind”. Patient feedback was incorporated into the final version of the educational materials.

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)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.311
Teacher spread0.299 · 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 designNot applicable
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

Citations6
Published2023
Admission routes2
Has abstractyes

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