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Record W4365459301 · doi:10.1080/0142159x.2023.2197134

Little Doctors: Agents of change in Indian rural communities

2023· article· en· W4365459301 on OpenAlexaff
Kalyani Premkumar, Rajkumar Ramasamy, Mary Ramasamy, Harini Aiyer

Bibliographic record

VenueMedical Teacher · 2023
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTamilAgency (philosophy)Medical educationPsychologyMedicineNursingSociologySocial science

Abstract

fetched live from OpenAlex

The child-to-child approach to health advocacy is one that draws on the strengths and agency of children to make a positive impact within their communities. The approach has been popularly used for health education in low- and middle-income countries. This article describes the 'Little Doctors' program that implemented the child-to-child approach in the towns of KC Patty and Oddanchatram, located in remote hilly regions of Tamil Nadu, India starting in 1986 to train middle- and high school children to respond to diseases prevalent in their communities along with practices for preventative measures. The program involved sessions that used a combination of creative instructional methods to engage students and provided take-home messages for them to act on with their families and community. The program was successful in creating a creative learning environment for children, offering a shift from the traditional methods of classroom instruction. Students who successfully completed the program were awarded certificates as 'Little Doctors' in their communities. Although the program did not conduct formal evaluations of the program effectiveness, students reported successfully recalling complex topics such as early signs of diseases like tuberculosis and leprosy that were prevalent in the community during the time. The program experienced several challenges and had to be discontinued despite its continued benefits to the communities.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.314
GPT teacher head0.536
Teacher spread0.222 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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