Bibliographic record
Abstract
Background: The theory of framing suggests that the media have the ability to influence how the public thinks about issues (Nelson, Oxley & Clawson, 1997; Chong & Druckman, 2007), by influencing what definitions, causal attributions, moral evaluations, and treatment recommendation the public considers applicable to an issue (Entman, 1993; Tewksbury & Scheufele, 2009). The theory of framing has been supported in studies of media representations of a variety of social issues. With particular relevance to this thesis, framing studies have suggested that health news often portrays the essence of health issues as highly alarming, with few efficacious treatment or coping options (Chang, 2012). The social issue this thesis focuses on specifically is infertility. Study 1: In Study 1, a content analysis is utilized to examine how Canadian print news frames infertility. One-hundred and fifty-seven Canadian print news articles that contained the key word “infertility” in the year 2012 were analyzed. Two independent coders read the articles, and coded each article using a predetermined coding strategy (Chang, 2012) for if/how infertility was framed with respect to: prevalence; need for alarm; severity; vulnerability; need for alertness; means of coping; causes; and possible solutions. Just over one-half of the articles employed alarm frames (n=80), and the vast majority of these met the criteria for categorization as high alarm (96%). The most commonly cited cause of infertility was delayed childbearing (41% of articles) and the most frequently presented way to cope with infertility was in vitro fertilization (IVF; 46% of articles). Infertility was most often constructed as a women’s issue. Study 2: Study 2 build on Study 1 by examining the influence that high alarm framing strategies in the presentation of infertility have on news consumer reactions to, and knowledge of, infertility issues. One hundred and thirty-nine male and female undergraduate students were randomly assigned to read news articles focusing on infertility judged to employ either high alarm framing strategies (high alarm condition, n=65) or low alarm framing strategies (low alarm condition, n=66). Participants in each condition read the assigned news articles and subsequently completed a self-administered questionnaire. The questionnaire included measures of: fear of infertility, perceived severity of infertility, perceived vulnerability to infertility, worry about infertility, prevention efficacy, coping efficacy, and knowledge about infertility. The participants in the high alarm condition evidenced higher levels of perceived vulnerability to infertility (p = .04), and marginally higher levels of worry about infertility (p = .075) than those in the low alarm condition. In contrast, participants in the low alarm condition relayed higher levels of infertility related knowledge than those in the high alarm condition (p= .001). Discussion: Canadian print news portrays infertility as a serious, a prevalent, an alarming and predominantly a women’s disease, and presents IVF as the principal means of coping. This partial depiction of infertility may not be promoting informed reproductive decision-making. Print news portrayal of infertility using high alarm framing strategies may induce higher worry about infertility and heightened levels of perceived personal vulnerability to infertility, while neglecting to relay pertinent knowledge about infertility. Implications for the societal understanding of infertility and the potential repercussions for informed reproductive decision-making are discussed.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.035 | 0.018 |
| Scholarly communication | 0.025 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".