"Western world's apart?" A comparison of patient information websites on depression in Canada, the United States and England
Bibliographic record
Abstract
Healthcare information differs among the United States, Canada and England. Through a critical discourse analysis of fifteen websites on depression from these countries, this thesis reveals how linguistic differences and differences in the websites’ use of visual features persuade readers of the merits of different treatments. The analysis reveals that the trends are complex. However, England’s websites lean more towards talking therapy, the United States’ websites emphasize pharmaceutical intervention, and Canadian websites endorse the use of antidepressants where they mention treatment. These findings are illustrated through a comparison of vocabulary, grammatical, visual and ordering features (building from Fairclough’s 1989 framework). This thesis also reveals that patients from the United States, Canada and England are portrayed as in possession of differing levels of importance in the treatment-decision-making process with physicians. Of particular significance is the Canadian websites’ portrayal of patient deferral to expert physicians. By frequently referencing “your doctor,” using marked grammar for healthcare experts, and providing a reader with limited information on treatment options, Canada’s websites assert a traditional biomedical model of power relations. The patient is secondary to the physician. The United States’ privatized healthcare system indicates that websites from the United States would portray patient input as especially significant. However, it is England’s websites that suggest a high level of patient influence. Stressing patient involvement on England’s websites is perhaps indicative of the NHS’s 2012 constitution, which emphasizes that decision-making should be based on a model of concordance not compliance (Segal 2007). These findings highlight how the different healthcare models of each country might affect the information provided to patients. Above all, this research raises questions about the role of patient information websites, and about the different discursive strategies that subtly persuade a reader to view depression, treatment and their input in decision-making differently. England, United States and Canada all make use of the DSM IV-TR diagnostic criteria and operate under a biomedical model of medicine, but these websites suggest that potentially depressed patients are approached differently in each of these countries.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".