Normalizing Masculinity: Explaining Processes, Factors, and Contexts That Influence How Rural Male Farmers Seek Health Information in Southwest Ontario
Bibliographic record
Abstract
Disproportionately high mortality and morbidity rates experienced by rural men are often related to the high prevalence of rural male farmers (RMFs) who are consistently exposed to chemicals, animal waste, and dust, or injured or killed while working. This dissertation aimed to explain processes by which RMFs seek health information (HI), and how these processes are influenced by rural social, cultural, political, and geographical factors.\nThree studies were conducted as part of this dissertation. The first study was a literature review that explored the relationship between rural men’s health, health information seeking (HIS) theory, and masculinity theory. The second study was a retrospective analysis of Ontario health policy and planning documents published since 2006 to establish the health policy context within which RMFs in Ontario seek HI. The third study integrated constructivist grounded theory and photovoice to identify and explain processes by which RMFs in southwest Ontario seek HI and factors that affect those processes.\nFindings of the literature review suggest that rural hegemonic masculinity – a socially desirable gender identity that values men’s toughness – may influence rural men to avoid HIS. Health policy and planning document analysis identified 13 documents published since 2006 that included RMFs’ health or health needs. Analysis indicated that health policy and planning document authors addressed RMFs as both: 1) token symbols of rural communities, and 2) key stakeholders to engage with to “mend fences” and improve strained relationships between healthcare providers and rural communities. Sixteen RMFs in southwest Ontario participated in the constructivist grounded theory-photovoice study. Participants revealed that their HIS was guided by an identity-related core process entitled ‘normalizing self as an RMF throughout HIS’, and that ‘normalizing’ was affected by rural social, cultural, geographical, and political factors.\nThese studies have implications for how rural communities, agricultural interest groups, health and non-health policy makers, and rural healthcare planners and providers can influence how RMFs seek HI. Future research is needed to understand how RMFs seek HI in different rural contexts, how rural communities can effectively support RMFs to engage in HIS, and how future health and non-health policy can promote RMFs’ health and HIS.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".