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Record W2072593562 · doi:10.1353/hpu.2014.0045

Impact of Depression on the Intensity of Patient Navigation for Women with Abnormal Cancer Screenings

2014· article· en· W2072593562 on OpenAlexaff
Ignacio I. De La Cruz, Karen M. Freund, Tracy A. Battaglia, Clara A. Chen, Sharon Bak, Richard Kalish, Barbara Lottero, Patrick Egan, Timothy Heeren, Andrea C. Kronman

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsWomen's Health Research Institute
FundersNational Institute on Minority Health and Health DisparitiesNational Cancer Institute
KeywordsDepression (economics)MedicineIntensity (physics)CancerPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Patient navigation is increasingly being used to support vulnerable patients to receive timely and quality medical care. We sought to understand whether patients with depression utilize additional patient navigation services after abnormal cancer screening. We compared depressed and non-depressed women using three different measures of intensity of patient navigation: number of patient-navigator encounters, encounter time, and number of unique barriers to care. The study population consisted of 1,455 women who received navigation after abnormal screening for breast or cervical cancer at one of six community health centers in Boston. Navigators spent a median of 60-75 minutes over one or two encounters per participant, with 49% of participants having one or more documented barrier to care. Depressed women did not differ in total numbers of encounters, encounter time, or unique barriers compared with non-depressed women. Our findings suggest that pre-existing depression does not predict which women will utilize additional navigation 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)0.001
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 score0.347
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.056
GPT teacher head0.358
Teacher spread0.302 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

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