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Record W4386482711 · doi:10.1186/s12884-023-05958-y

Using the COM-B framework to elucidate facilitators and barriers to COVID-19 vaccine uptake in pregnant women: a qualitative study

2023· article· en· W4386482711 on OpenAlexfundno aff
Lynsey Patterson, Emma Berry, Carole Parsons, Bronagh Clarke, Alison Little, Jillian Beggs, Antony Chuter, Tracy Jackson, Yingfen Hsia, Hannah McGrath, Catherine Millman, Siobhán Murphy, Declan Bradley, Sarah Milligan

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilPublic Health AgencySwansea UniversityImperial College LondonQueen's University BelfastUniversity of OxfordUK Research and InnovationQueen's UniversityAlan Turing Institute
KeywordsFocus groupThematic analysisMedicinePsychological interventionFamily medicinePopulationQualitative researchNursingEnvironmental health

Abstract

fetched live from OpenAlex

Since April 2021, COVID-19 vaccines have been recommended for pregnant women. Despite this, COVID-19 vaccine uptake in this group is low compared to the non-pregnant population of childbearing age. Our aim was to understand barriers and facilitators to COVID-19 vaccine uptake among pregnant women in Northern Ireland using the COM-B framework, and so to make recommendations for public health interventions. The COM-B proposes that human behaviour is influenced by the extent to which a person has the capability, opportunity, and motivation to enact that behaviour. Understanding the factors underpinning behaviour through this lens helps discern what needs to change to change behaviour, therefore supporting the development of targeted interventions.This study consisted of eight semi-structured interviews with new/expectant mothers who did not receive a COVID-19 vaccine dose while pregnant since April 2021, and a focus group with five participants who received at least one COVID-19 vaccine dose while pregnant. Interview and focus group data were analysed using semi-deductive reflexive thematic analysis framed by a subtle realist approach. The COM-B was used to categorise codes and subthemes were developed within each COM-B construct.Within Psychological Capability, subthemes captured the need for consistent and reliable COVID-19 vaccine information and access to balanced and jargon-free, risk-benefit information that is tailored to the pregnant individual. The behaviour/opinions of family, friends, and local healthcare providers had a powerful influence on COVID-19 vaccine decisions (Social Opportunity). Integrating the COVID-19 vaccine as part of routine antenatal pathways was believed to support access and sense of familiarity (Physical Opportunity). Participants valued health autonomy, however experienced internal conflict driven by concerns about long-term side effects for their baby (Reflective Motivation). Feelings of fear, lack of empathy from healthcare providers, and anticipated guilt commonly underpinned indecision as to whether to get the vaccine (Automatic Motivation).Our study highlighted that the choice to accept a vaccine during pregnancy generates internal conflict and worry. Several participants cited their concern was primarily around the safety for their baby. Healthcare professionals (HCPs) play a significant part when it comes to decision making about COVID-19 vaccines among pregnant women. HCPs and pregnant women should be involved in the development of interventions to improve the delivery and communication of information.

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)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.280
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.400
Teacher spread0.335 · 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.

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

Citations16
Published2023
Admission routes1
Has abstractyes

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