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Record W3201000947 · doi:10.5539/gjhs.v13n10p91

Determining Reasons Affecting the Late Treatment of Congenital Talipes Equinovarus: A Qualitative Study

2021· article· en· W3201000947 on OpenAlexvenueno aff
Panji Sananta, Tita Hariyanti, Ledy Kumala Devi, I Gusti Ngurah Arga Aldrian Oktafandi, Felix Cendikiawan

Bibliographic record

VenueGlobal Journal of Health Science · 2021
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsMisinformationMedicineSocioeconomic statusCongenital talipes equinovarusClubfootQualitative researchPhysical therapyDeformityFamily medicinePediatricsSurgeryPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Neglected congenital talipes equinovarus (CTEV) is a serious problem commonly found in developing countries. This deformity has fatal impacts, including disabilities and educational limitations. Moreover, cases of neglected CTEV are still frequent in Indonesia as one of the developing countries. Thus, this study aims to investigate the reasons behind the late treatment of CTEV. METHODS: This is a qualitative study conducted throughout September 2019. The subjects are six parents of patients with neglected CTEV who are currently being treated in our hospital. The study was conducted by performing an in-depth interview with the patient’s parents to analyze why they delay checking their child to an orthopaedic surgeon. The results were then grouped into themes. RESULTS: The reasons for the delay were multifactorial. Four subjects had more than one reason why they delayed checking their child to an orthopaedic surgeon. The reason for the delay were socioeconomic factors (3 subjects), medical-related problems (3 subjects), intentionally delayed or refused to seek medical care (2 subjects), and misinformation from the previous physician (3 subjects). CONCLUSIONS: The reasons for the late treatment of CTEV were socioeconomic factors, medical-related problems, intentional factors, and misinformation.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.087
GPT teacher head0.445
Teacher spread0.358 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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