Content analysis of Canadian newspapers articles and readers’ comments related to schizophrenia
Bibliographic record
Abstract
Schizophrenia is a complex biochemical brain disorder with significant prevalence rates. People suffering from schizophrenia are stigmatized in the society at both the personal and institutional level. With newspapers (print and electronic) serving as the voice of the masses, people with schizophrenia are often negatively represented. In this study we collected all articles of the year 2014 from top-10 online available English language Canadian newspapers by using schizophrenia as a keyword. Readers’ comments and social media sharing information of each of the articles were also collected. Inclusion-exclusion criteria and coding schema were developed to select and categorize relevant articles and comments. Statistical analyses were performed to see the relation of social media sharing with different categories of articles. Our study revealed that news of crime and violence by people with schizophrenia hold the highest representation; subsequently, in these type of articles most of the readers’ comments were negative. On the other hand, readers mentioned positive comments and showed sympathy for those who are suffering from the stigma. This study unveiled how schizophrenia is presented in the articles of top-10 online available English language Canadian newspapers. Also, the analysis of readers’ comments and sharing in social media were a reflection of readers’ reaction to schizophrenia and people with schizophrenia.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".