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Record W3108161948 · doi:10.2196/21443

Risk Perception of Health Professionals in Intrapartum Care Decisions: Protocol for a Mixed Methods Study

2020· article· en· W3108161948 on OpenAlexvenueno aff
Nina Peterwerth, Margareta Halek, Sabrina Tulka, Rainhild Schäfers

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupRisk perceptionMedicineQualitative researchPsychological interventionHealth careNursingPerceptionChildbirthExploratory researchFamily medicinePsychologyPregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: Risk perception plays an important role in decision-making processes. Differences in obstetric intervention rates suggest that, in addition to medical indications, the risk perception of obstetric health professionals might have a major influence on their decision-making process during childbirth. Although studies have investigated whether risk perception affects the role of midwifery or influences decision making during childbirth, little is known about what obstetric health professionals actually perceive as risk or risky situations and whether different risk perceptions lead to more interventions during intrapartum care. OBJECTIVE: The objective of this study is to understand the association of risk perception and the decision-making processes of obstetric health professionals (midwives and obstetricians) in Germany during intrapartum care. The study has 3 specific aims: (1) gain insight into what obstetric health professionals perceive as risk in the German clinical setting, (2) assess the extent to which personal and systemic factors have an impact on obstetric health professionals' risk perception, and (3) investigate whether different perceptions of risk are associated with different decisions being made by obstetric health professionals. METHODS: This is an exploratory sequential mixed methods study with 2 phases, a qualitative followed by a quantitative phase. In the first phase, qualitative data are collected and analyzed by conducting focus group discussions and applying qualitative content analysis to address aim 1. In the second phase, for aims 2 and 3 and to help explain the qualitative results, quantitative data are collected and analyzed by conducting an observational study using case vignettes within a survey constructed on the basis of the qualitative results. RESULTS: Enrollment in the first (qualitative) phase began in July 2019, and data collection and analysis have been completed. The second (quantitative) phase is currently planned, and data collection is expected to start in December 2020. First results of the qualitative phase are expected to be submitted for publication in 2020, with completion of the second phase scheduled for 2021. CONCLUSIONS: This mixed methods study will examine the perception of risk and its association with the decision-making processes of obstetric health professionals during their care of women in childbirth. The rationale for this approach is that the qualitative data and their analysis explore participants' views in more depth, while the quantitative data will help to provide and explore a general understanding of the research problem. The results are expected to be relevant to health care professionals, policymakers, and educational institutions in order to minimize underuse, overuse, and misuse of interventions during intrapartum care. TRIAL REGISTRATION: German Clinical Trials Register DRKS00017172; https://tinyurl.com/y2zoowkx. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21443.

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)0.071
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.071
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.059
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0040.005
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0030.003
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0610.012

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 head0.572
GPT teacher head0.727
Teacher spread0.155 · 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 designQualitative
Domainnot available
GenreProtocol

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

Citations7
Published2020
Admission routes1
Has abstractyes

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