MétaCan
Menu
Back to cohort
Record W2785805892 · doi:10.32413/pjph.v7i4.76

KNOWLEDGE AND PRACTICE OF LADY HEALTH VISITORS REGARDING CERVICAL CANCER IN PUBLIC SECTOR MATERNAL AND CHILD HEALTH CENTERS IN LAHORE, PAKISTAN

2018· article· en· W2785805892 on OpenAlexaff
Noreen Zafar, Anam Zahira, Moeen ud din, Iffat Naz, Anwar Choudhary

Bibliographic record

VenuePakistan Journal of Public Health · 2018
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsContech (Canada)
Fundersnot available
KeywordsCervical cancerMedicineFamily medicinePublic healthDeveloping countryCross-sectional studyCommunity healthGynecologyNursingCancer

Abstract

fetched live from OpenAlex

Background: Globally cancer has grown as public health issue in developing and developed countries, especially swiftly spreading in low income areas because there are inadequate means for prevention, diagnosis and treatment. Methods: A hospital based cross-sectional research study was conducted from 11th December to 31st, 2016. There are total 52 centers in Lahore are working to provide the basic MCH health services in the community headed by Lady Heath Visitors. Hence; all Lady Heath Visitors were selected to participate in the study and were interviewed at their center by a trained research associate. Results: All respondents had basic knowledge about cervical cancer. 96.1% respondents were aware about speculums used in gynecology examination, and rests were not aware. 94.2% were familiar with indication of cervical cancer, 92.3% were aware about indication for doing a speculum examination and 82.7% told that they were able to diagnose cervical cancer in routine gynecological examination and same percentage reported that they had been taught how to do speculum examination. Conclusion: Majority of staff had insufficient knowledge regarding cervical cancer prevalence, treatment and prevention. Practices of the health providers were not up to minimum standard of any basic health services.

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)0.011
metaresearch head score (Gemma)0.001
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 score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.073
GPT teacher head0.444
Teacher spread0.371 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePakistan Journal of Public HealthSame topicCervical Cancer and HPV ResearchFrench-language works237,207