Exploration of Loneliness Among Black Older Adults
Bibliographic record
Abstract
Background: Loneliness is a public and social issue affecting older adults, but in varying degrees across ethnic groups. Black older adults (BOAs) are more prone to loneliness because they have unique and accumulated factors (e.g., low socioeconomic status, high number of chronic conditions) that predispose them to loneliness. This review aims to describe the extent and the nature of research activities on loneliness and identify the contributory factors to loneliness among BOAs as presented in the global literature. Methods/Design: We will follow the five steps of Arksey and O’Malley’s (2005) framework to search multiple databases from inception till June 2021. MeSH terms and keywords, e.g., “older adults,” “blacks,” and “loneliness,” will be adopted for several databases, including CINHAL, Ageline, PsychINFO, Cochrane Central Registers of Control Trials, PubMed, Web of Science, Social Science Abstract. Multiple reviewers will independently screen citations (title/abstract and full text) and extract data using predefined inclusion and exclusion criteria. “Best fit” framework synthesis using the six social provisions of Weiss’ framework as a priori themes will guide the data analysis. Discussion: This review will inform policy development around contributory factors for loneliness among BOAs and the most relevant issues on loneliness related to BOAs.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".