Critical Food Safety Violations in Food Facilities by Regions Across Calgary Communities From 2022 Versus 2023
Bibliographic record
Abstract
This project was a clinical practice guideline (CPG) for mental health providers to expand telepsychiatry services to veterans who are homeless in a day treatment center. The practice problem was the lack of a standardized approach to increase access to mental health care. This CPG supports nurses in implementing evidence-based interventions to reduce mental health disparities and promote equitable care delivery for veterans who are homeless. The guideline was aimed to ensure safe, effective, and coordinated mental health care through integration with interprofessional teams. The project question was: Does the evidence support development of a CPG for provision of telepsychiatry services for veterans who are homeless that receives a quality score using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument and is approved for use in the practice setting by end users? The purpose of this project was to develop a CPG for providers on use of telepsychiatry to improve mental health care delivery for veterans who are homeless. I used the Johns Hopkins evidence-based model to collect, organize, and analyze the 22 items of evidence that supported this project. An expert panel evaluated the quality of the CPG across six domains using the AGREE II instrument. Domain scores from the two members of the expert panel ranged from 94.4% to 100%, resulting in a high-quality score. The global assessment scores were 97.4% and 96.4%, confirming high methodological quality and recommendation for use without modification. The development of a CPG for telepsychiatry enhances access to equitable, evidence-based mental health care for veterans who are homeless at the project site. This project contributes to reducing mental health disparities, promoting health equity, and advancing social justice for vulnerable and marginalized populations.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".