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P1734: LESSONS FROM HIV- HAEMOPHILIA CRISIS AND CURRICULAR LEARNING IN POST COVID TIMES

2022· article· en· W4283395768 on OpenAlexaboutno aff
Mallika Sekhar, C. Merrion, Sushrut Jadhav

Bibliographic record

VenueHemaSphere · 2022
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsHaemophiliaContext (archaeology)Medical educationCurriculumPsychologyMedicinePandemicFamily medicineCoronavirus disease 2019 (COVID-19)PediatricsPedagogyHistory

Abstract

fetched live from OpenAlex

Background: The COVID19 pandemic has posed challenges to clinical decision-making in the context of prolonged uncertainties in clinical, scientific and social areas. The HIV pandemic of 1980s represents a similar period with data and insights acquired over years. Haematologists are familiar with the notion of uncertainty. In particular, haemophilia care during the early 1980s posed specific and unique problems in clinical decision-making. An understanding of professional and personal concerns faced by doctors at the time can contribute to learning and continued professional development. Aims: Compile, structure and analyse audio interviews from physicians involved in haemophilia care in the 80s to generate learning material for curricular learning. Methods: Between June and November 2019 oral history interviews were conducted using a semi-structured questionnaire with 16 senior haemophilia doctors (13 male, 3 female) from Australia, Canada, France, Italy, Netherlands. 28 person-hours of interview material was analysed using pre-established criteria1. Audio recordings and transcripts from UK Infected Blood Inquiry2 held between October 2020 and January 2021 were analysed for content. 18 oral evidences comprising approximately 286 person-hours were examined to extract themes addressing difficulties and dilemmas in clinical decision-making. Following ethical approval, an audio-podcast series3 was produced to address specific domains and themes. Results: Audio material was compiled into segments of audio clips that identified topics across four areas of practice: diagnostic tests, clinical decision making, shared axes of care and personal experiences of adverse outcomes. Material from 6 oral history interviews (Italy, France, Netherlands) was compiled to illustrate cross-national themes and personal dilemmas. These themes were mapped on relevant curricular domains defined by the GMC. (Table 1) The compilation was interspersed with an audio commentary by the first author and published as a podcast series comprising 8 podcasts over approximately 4 hours. Access was via free public platforms. Between November 2021 and February 2022, 472 downloads were documented on one of four platforms. Six medical students and six faculty provided triangulation including feedback. Their feedback confirmed the pedagogic relevance of these podcasts for undergraduate students across years 1 to 5 as part of curricular learning. Feedback from postgraduate trainees in Haematology, other specialities and consultants confirmed the relevance of this material in their continued professional development. Image:Summary/Conclusion: •Our study generates data that contribute to future research on physicians exposed to prolonged uncertainties in clinical and laboratory practice. •Lessons from haemophilia-AIDS crisis are relevant for some aspects of COVID19pandemic. •Recent history of medical events provides a valuable resource to enhance teaching. •Oral history interviews and oral evidence submitted at an inquiry differ in nature but provide complimentary insights. •Podcasts can provide an effective platform for curricular and self-directed learning for graduate and postgraduate learning, worldwide. •A structured platform to catalogue difficult decisions can provide haematologists and others a frame of reference in making difficult decisions. Future research could address the scope and applicability of this pragmatic approach to history of medicine. Ref: 10.1111/hae1.3967 https://www.infectedbloodinquiry.org.uk/ https://thebitterpillpodcasts.libsyn.com

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.044
GPT teacher head0.403
Teacher spread0.359 · 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 teacher head, not a consensus.

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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