Understanding and Addressing Racial Disparities in Health Care: Exploring the Information Seeking Behaviours of Racialized Women Managing Chronic Pain
Bibliographic record
Abstract
In the area of health information-seeking behaviour (HISB), racialized women require a variety of resources for managing their chronic health conditions, including accessing information from medical professionals. However, this paper explores the ways in which their information needs have been limited due to racial biases or microaggressions from doctors and specialists. A main objective of this study is to establish a framework on which future research can be built. Using Sonnenwald’s (2001) Information Horizons Interview (IHI) methodology, I conducted in-depth interviews with three women living with chronic health conditions, where the participants provided verbal and graphical articulations of their health-related information horizons. The analysis suggests that (1) racialized women rely on the internet, community and medical professionals for information related to managing their conditions; (2) the information received is influenced by racial biases, which devalues the information relevant to care; (3) participants make substantial use of interpersonal resources to make up for this lack of information and; (4) they typically find this information through information grounds and information encountering with other racialized women. Since this research is for participants and not solely about participants, it explores implications and strategies for moving forward in the HISB needs for racialized women in Toronto. Keywords: racialized women, chronic health conditions, information-seeking behaviour, intersectionality, health inequities, racial disparities, health literacy
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".