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Record W4210556261 · doi:10.1186/s12913-022-07522-4

Impact of birth tourism on health care systems in Calgary, Alberta

2022· article· en· W4210556261 on OpenAlexaffabout
Simrit Brar, Mruganka Kale, Colin Birch, Fiona Mattatall, M VAZE

Bibliographic record

VenueBMC Health Services Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsHealth informaticsNursing researchHealth administrationMedicinePublic healthHealth careHealth services researchEnvironmental healthNursingEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Birth tourism refers to non-resident women giving birth in a country outside of their own in order to obtain citizenship and/or healthcare for their newborns. We undertook a study to determine the extent of birth tourism in Calgary, the characteristics and rationale of this population, and the financial impact on the healthcare system. METHODS: A retrospective analysis of 102 women identified through a Central Triage system as birth tourists who delivered in Calgary between July 2019 and November 2020 was performed. Primary outcome measures were mode of delivery, length of hospital stay, complications or readmissions within 6 weeks for mother or baby, and NICU stay for baby. RESULTS: Birth Tourists were most commonly from Nigeria (24.5%). 77% of Birth Tourists stated that their primary reason to deliver their baby in Canada was for newborn Canadian citizenship. The average time from arrival in Calgary to the EDD was 87 days. Nine babies required stay in the neonatal intensive care unit (NICU) and 3 required admission to a non NICU hospital ward in first 6 weeks of life, including 2 sets of twins. The overall amount owed to Alberta Health Services for hospital fees for this time period is approximately $694 000.00. CONCLUSION: Birth Tourists remain a complex and poorly studied group. The process of Central Triage did help support providers in standardizing process and documentation while ensuring that communication was consistent. These findings provide preliminary data to guide targeted public health and policy interventions for this population.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
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 head0.100
GPT teacher head0.541
Teacher spread0.441 · 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

Citations16
Published2022
Admission routes2
Has abstractyes

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