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Record W2991251146

Case 3 : The Missing Four Million: Working to Increase the Case Finding Rate for People with TB

2019· article· en· W2991251146 on OpenAlexaboutno aff
Ashley Fantauzzi, Taryn Russell, Shannon L. Sibbald

Bibliographic record

VenueScholarship@Western (Western University) · 2019
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsPsychology
DOInot available

Abstract

fetched live from OpenAlex

Paru Hari, an Accredited Social Health Activist (ASHA), lives in Bihar, India, one of the poorest states in the country. Paru is involved in daily outreach within her community to facilitate community member access to health care facilities, administer medications, treat minor ailments, and generate health awareness. The majority of her work involves antenatal checkups, immunizations, and mild sickness treatments. However, with Bihar reporting approximately 70,000 new cases of tuberculosis (TB) annually and many cases going unreported and undiagnosed (Fathima, Varadharajan, Krishnamurthy, Ananthkumar & Mony, 2015; RESULTS Canada, 2018b), Paru decided to take action. She proposed that ASHAs act as TB educators and household screeners for patients who have TB because she was tired of watching people in her community suffer and die from a treatable disease. Paru decided to visit Dr. Tisha Guru, Bihar state’s Regional ASHA Program Director, to share her concerns about how best to integrate TB educational activities and household screening programs into her daily routine. For Paru to gain a clear understanding of what she needs to know to identify TB patients and what they require during diagnosis and treatment, Dr. Guru suggested that she accompany patients from the initial stages of their diagnosis through to treatment. Although Paru did not have an extensive medical background, she knew that the ASHA program required a great deal of funding to ensure it was sustainable and that the necessary resources were available for TB testing and care to be integrated into their daily work. Paru knew action needed to be taken, not only to continue the ASHA program but, more importantly, to help patients who were being overlooked by the current health care system. Paru worked alongside Dr. Guru to identify the key stakeholders who could effectively communicate the critical need for improved TB surveillance, educational activities, and household screening programs into the services ASHAs provided.

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)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0110.002
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0170.002

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 head0.151
GPT teacher head0.357
Teacher spread0.207 · 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 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
Published2019
Admission routes1
Has abstractyes

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