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Record W2898352580 · doi:10.5539/gjhs.v10n11p169

Evaluation of the Integrated Primary Care Clinic Into Behavioral Care Setting

2018· article· en· W2898352580 on OpenAlexvenueno aff
Jarman Alqahtani, Daniel J. West

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsnot available
FundersUniversity of Scranton
KeywordsMedicineEmergency departmentPrimary careHealth careFamily medicinePopulationPrimary health careNursingEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: The study has aimed to explore the process, outcomes of primary care, and barriers that make the primary care access difficult for the patients. DESIGN & SETTING: The study has utilized quantitative and qualitative approach and collected data from the clinic and patients. Patient survey was conducted to ask the patients about the possible reasons, which prevent them from accessing primary care services in the past. RESULTS: The mean age of patients was 46 years, among which majority (65%) were males. The results showed that education was the significant factor in determining the health status of a specific population. The clinic was successfully integrated into the behavioral health care setting. Many patients had been enrolled in the clinic for the first time with the help of a care manager that facilitated the identification of those patients. Most commonly, transportation was the main barrier for those populations for not seeking the primary care services. Emergency department use significantly declined after the implementation of the new model that reduced the cost of health services dramatically in a short period of time i.e. 6 months. CONCLUSION: There are susceptical gaps within the fragmented care due to high rates of physical health conditions. Majority of the patients in the study sample were satisfied with the new model; therefore, the new model was termed as effective and efficient.

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)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.205
GPT teacher head0.578
Teacher spread0.373 · 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 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

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