Identifying and mapping innovation(s) for screening and diagnosis of sexually transmitted and blood-borne infections (STBBIs) during the COVID-19 Pandemic: A Scoping Review
Bibliographic record
Abstract
Objective: The main objective of this scoping review is to identify and map available peer-reviewed literature on person-focused innovation(s) for screening and diagnosis of sexually transmitted and blood-borne infections (STBBIs) during the COVID-19 pandemic in Organisation for Economic Co-operation and Development (OECD) countries. Introduction: Timely access to STBBI care and support is dependent on a confirmed medical diagnosis, the first part of the 'cascade of care.’ Access to STBBI testing has been impacted during the COVID-19 pandemic. Service providers in Atlantic Canada have expressed concern regarding this impact on missed or delayed diagnosis and its impacts on linkage to care and treatment. Inclusion criteria: This scoping review will consider peer reviewed original research articles describing person-focused innovation(s) in the provision of screening and diagnosis of STBBIs (including human immunodeficiency virus, acquired immunodeficiency syndrome, chlamydia, gonorrhoea, hepatitis B, hepatitis C, and syphilis) during the COVID-19 pandemic in Canada and other OECD countries. Research conducted prior to the pandemic and gray literature will not be considered for this review. Methods: The databases to be searched include MEDLINE (Ovid), CINAHL (EBSCO), Embase (Elsevier), Social Services Abstracts (ProQuest), and Sociological Abstracts (ProQuest). Each study for title and abstract screening will be completed by two independent reviewers. Full text screening and data extraction will be completed by two of four independent reviewers. Any conflicts that arise will be resolved by one of two senior authors. Results: Data will be presented in a tabular or diagrammatic form, with a narrative summary.
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 | 0.028 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.029 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".