Health information behavior research with marginalized populations
Bibliographic record
Abstract
ABSTRACT As part of an increasingly vibrant area of research, information behavior scholars have shown that traditionally marginalized populations (e.g., older adults, LGBTQ people, people of color, low‐income people and people with chronic diseases or disabilities) may have distinct health information needs and information behaviors. These differences may arise from unique patterns in marginalized groups' life experiences, health risks and burdens, social networks and available resources, as well as dynamics of social marginalization and exclusionary service design. This subfield of information studies challenges established notions of health information‐seeking behaviours to further develop theories and models, as well as propose new models for information services and technologies. The unique characteristics of marginalized populations have necessitated the development of novel research approaches and methods, as well as interdisciplinary collaborations and community‐based partnerships. This panel invites audience members to think critically about what it means to engage marginalized populations in research and the methods and approaches needed to do so in a health context. It will also allow participants to broaden their understanding about the health information‐seeking behaviors of marginalized populations. Panelists will introduce and contextualize marginalized populations' health information‐seeking behaviours and explore potential or existing connections between themes from a variety of disciplines. Following a brief introduction and presentations from five panelists (who are themselves exploring marginalized populations' health information‐seeking behaviours), there will be an open discussion session with the audience in a World Café format.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.016 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".