Systematic review of human living libraries mapped to the social determinants of health
Bibliographic record
Abstract
Abstract Background Human living libraries (HLL) promote engagement with individuals representing societal groups often facing prejudice or discrimination. Over the past two decades, the concept of HLLS has expanded in healthcare literature. HLLs aim to provide a mechanism that challenges stereotypes and reduces stigma, facilitating one-to-one conversations between a human book and a reader. This review seeks to understand the impact of HLLS, focusing on diversity, equity, and inclusion. Methods Systematic searches [2000-2024] were conducted across 10 databases [ASSIA, CINAHL, Embase, ERIC, LISA, PsycINFO, PubMed, Scopus, Sociological Abstracts, Web of Science]. No restrictions on language or study design were applied. Google Translate and DeepL facilitated the translation of non-English papers. A total of 541 records were identified, and 281 records were uploaded to Covidence after deduplication. Robust review methods were adopted for screening, quality assessment (using Mixed Methods Assessment Tool), equity mapping (using Progress Plus framework) and narrative synthesis. Results Preliminary data from 27 studies (31 records) includes 12 countries: Canada (6/27), USA (5/27), Spain, China, Hong Kong, Russia, the Philippines, Poland, UK, Hungary, Turkey, and Taiwan. Most HLLs were developed in university settings, public libraries, art galleries, and secondary schools. Eight studies (29.6%) report HLL development as part of curriculum development, and one study presents HLL as supporting global students adapting to a new university setting, learning social norms, and supporting engagement. 70.4% (18/27) of studies align with the Progress Plus framework, highlighting diversity and inclusion across specified occupations, genders, religions, languages, cultures, education, and broader diversity. Conclusions Preliminary findings emphasise the inclusivity of HLLs and building capacity for public health education and research that align with social determinants of health. Key messages • Human Living Libraries offer creative mechanisms using human books that support real-world social inclusion and diversity conversations globally. • Human Living Library development requires co-designed development [virtual, in-person events or in curricula developments] to ensure culturally sensitive approaches and limiting of tokenism.
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 | 0.021 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".