Sex and Gender Differences in Health Information Needs for Arthritis Patients
Bibliographic record
Abstract
Background: The onset and progression of both degenerative and inflammatory arthritis can be affected by sex and gender.Both age and gender can affect people's decisions about their health.Men, for instance, may employ fewer, and less varied coping mechanisms than women.Objectives: To focus on the health information needs of arthritis patients and investigate the experiences of arthritis patients from various sexes and genders, as well as how they think their sex and/or gender might influence their health information needs.It also sought to determine whether they would benefit from a health information intervention, and if so, what kind of intervention they would like and how it should be delivered.Methods: This study followed a qualitative approach, interpretive description, where 13 participants were recruited from Roth McFarlane Hand & Upper Limb Centre at St. Joseph's Health Centre.The inclusion criteria were patients with confirmed arthritis aged 18-75, who could speak and understand English, and could consent to participate.Thematic analysis was conducted.Results: Ten overarching themes were identified with various subthemes across 13 transcripts.These themes were: positive therapeutically alliance with physicians, need for online resources, men are more reluctant to seek help, systemic challenges to accessing healthcare, patients' health information needs, perceived facilitators, level of satisfaction with the information or services provided by physicians, gender affects information needs but not the ability to establish a therapeutic alliance, dire need for more access to arthritis information programs, and mixed understanding about the meaning of sex/gender.Conclusion: The demands of patients for health information are influenced by their gender and/or sexual orientation.Given patients' challenges while accessing health information, it is critical to adopt a patient-centred strategy that focuses on their needs and allows them to express their opinions.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".