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Record W4251920422 · doi:10.24124/2018/58757

Prevention of RhD alloimmunization in Northern British Columbia

2018· dissertation· en· W4251920422 on OpenAlexafffundabout
Trina Fyfe

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersCanadian Medical Association
KeywordsMedicineQualitative researchGuidelineHealth careStakeholderPregnancyNursingFamily medicinePolitical sciencePublic relations

Abstract

fetched live from OpenAlex

Despite best practice guidelines, international evidence suggests that the provision of anti-D prophylaxis to RhD negative pregnant women is suboptimal. Missing from the literature is research exploring the factors that perpetuate suboptimal care and continue to put RhD negative pregnant women at risk for RhD alloimmunization. The purpose of this study was to understand why RhD negative pregnant women continue to be at risk for RhD alloimmunization in northern BC. The specific research questions were: How do healthcare providers make decisions regarding the care of RhD negative pregnancies in northern BC? How do RhD negative women in northern BC experience pregnancy? A qualitative approach utilizing interpretive description was used to address the need for the development of rural centric clinical guidelines. Interviews were conducted with RhD negative women about pregnancy and healthcare providers’ experiences in caring for RhD negative pregnancies in northern BC. A Stakeholder Committee guided the research process and provided insight into data analysis to ensure applicability to practice. A qualitative approach with these two populations has provided a greater understanding into the depth of quality of care for RhD negative pregnancies and the decisions that inform patient safety by revealing nuances of care that lead to potential miscommunication and near misses. Recommendations into guideline adaptation, decision-making and health literacy in rural healthcare settings are presented.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.248
Teacher spread0.241 · 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".

Quick stats

Citations1
Published2018
Admission routes3
Has abstractyes

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