MétaCan
Menu
Back to cohort
Record W4404453780 · doi:10.35680/2372-0247.1896

An Exploratory Qualitative Study of Perinatal Experiences in an Acute Setting during Early Phases of the COVID-19 Pandemic

2024· article· en· W4404453780 on OpenAlexaff
Sharon Hoosein, Pamela Winchester, Stephanie Babinski, Naomi Smith, Susan Law

Bibliographic record

VenuePatient Experience Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMcMaster UniversityUniversity of TorontoTrillium Health Centre
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Patient experienceExploratory researchQualitative research2019-20 coronavirus outbreakMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)NursingPsychologyHealth careVirologySociologyDiseaseEconomic growthInternal medicineSocial scienceEconomicsOutbreak

Abstract

fetched live from OpenAlex

The COVID-19 pandemic was highly disruptive for people delivering babies in-hospital and for obstetrical healthcare professionals. The purpose of this study was to explore the experiences of people with or without COVID-19 giving birth in a community-based hospital to provide patient insight to obstetrical care providers regarding the services/policies used during the pandemic. Nine interviews were conducted with participants within six months of giving birth in-hospital – four who tested positive for COVID-19 and five who tested negative. Seven themes were identified in the analysis: conflicting emotions; experiences of COVID-related protocols; altered experiences of pregnancy and birth; other aspects of in-hospital care; support from family and friends; interactions and communications with the healthcare team; and seeking information. Results were positively received by the perinatal clinical team and changes were identified to further improve experiences of care. A deeper understanding of patients' lived experiences of hospital services available during public health emergencies can offer important, actionable information for healthcare providers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.519

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.149
GPT teacher head0.515
Teacher spread0.367 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuePatient Experience JournalSame topicCOVID-19 and Mental HealthFrench-language works237,207