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Record W4416048697 · doi:10.17615/pfj1-jm49

How COVID-19 has impacted integrated care practice: lessons from the frontlines

2025· article· en· W4416048697 on OpenAlexaboutno aff
Lisa de Saxe Zerden, Zachary Cooper

Bibliographic record

VenueUNC Libraries · 2025
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated careHealth carePrimary careQuarter (Canadian coin)WorkflowPrimary health careIntegrated servicesHealth care deliveryWork (physics)

Abstract

fetched live from OpenAlex

Primary care systems are a mainstay for how many Americans seek health and behavioral health care. It is estimated that almost a quarter of behavioral health conditions are diagnosed and/or treated in primary care. Many clinics treat the whole person through integrated models of care such as the Primary Care Behavioral Health (PCBH) model. COVID-19 has disrupted integrated care delivery and traditional PCBH workflows requiring swift adaptations. This paper synthesizes how COVID-19 has impacted clinical services at one federally qualified health center and describes how care has continued despite the challenges experienced by frontline behavioral health providers.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.263
GPT teacher head0.402
Teacher spread0.139 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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