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Record W4385684231 · doi:10.2196/47018

Testing the Impact of Phone Texting Reminders for Children's Immunization Appointments in Rural Cameroon: Protocol for a Nonrandomized Controlled Trial

2023· article· en· W4385684231 on OpenAlexvenueno aff
Yayah Emerencia Ngah, Ghazal Raoufi, Maral Amirkhani, Ashkan Esmaeili, Rasa Nikooifard, Shidrokh Ghaemi Mood, Ava Rahmanian, Minyahil Tadesse Boltena, Eresso Aga, Ujjwal Neogi, George Mondinde Ikomey, Ziad El‐Khatib

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVaccinationDescriptive statisticsPandemicFamily medicinePhoneIntervention (counseling)ImmunizationPediatricsMedical emergencyCoronavirus disease 2019 (COVID-19)NursingDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Globally, over 20 million children are unvaccinated and over 25 million missed their follow-up doses during the COVID-19 pandemic; thus, they face vaccine-preventable diseases and unnecessary deaths. This is especially the case for those with HIV or living in vulnerable settings. Using cell phones to send reminders to parents has been shown to improve vaccination rates. OBJECTIVE: We aim to determine whether implementation of an automated SMS reminder will improve child vaccination rates in a turbulent, semiurban/semirural setting in a low-income country. METHODS: This will be a nonrandomized controlled trial that will be conducted at Azire Integrated Health Centre, Bamenda, Cameroon. RESULTS: A total of 200 parents per study group (aged over 18 years) who are registered at the clinic at least one month prior to the study will be recruited. The intervention group will receive 2 reminders: 1 week and 2 days prior to the scheduled vaccination. For those who miss their appointments, a reminder will be sent 1 week after their missed appointment. The control group will receive the regular care provided at the clinic. Baseline information, clinical visit data, and vaccination records will be collected for both groups. Descriptive statistics will be used to summarize baseline characteristics between and within clusters and groups. The Fisher exact test will be used to compare parent-child units who return for follow-up visits (as a percentage) and children vaccinated as scheduled (as a percentage) between the study groups. Finally, we will compare how many members of both study groups return for 1 follow-up visit using Kaplan-Meier survival analysis. CONCLUSIONS: Due to limited effective child vaccination interventions in unstable settings, this study will be of high importance for suggesting a holistic approach to improve child vaccination and public health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47018.

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)0.034
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.055
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.029
Meta-epidemiology (narrow)0.0070.003
Meta-epidemiology (broad)0.0120.005
Bibliometrics0.0030.003
Science and technology studies0.0030.004
Scholarly communication0.0040.004
Open science0.0040.002
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0550.010

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 head0.231
GPT teacher head0.565
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations1
Published2023
Admission routes1
Has abstractyes

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