OPEN EDITORIAL Are we overlooking the qualitative ‘look ’ of obesity?
Bibliographic record
Abstract
Summit held recently in Toronto, along with the traditional speeches and awards, a woman who formerly had obesity shared her personal story. Emotional, heart-felt, and humanizing, her experience of living with obesity as a child, a professional, a wife, and an artist provided a detailed and personal view of her ongoing personal struggles with her weight, which set the tone for the meeting over the next few days. Her story and others like it can provide rich insight into individuals ’ perspectives of obesity and weight management. In our view, these perspectives have been under-represented in the field of obesity research where numbers from quantitative research often take precedence over meanings derived from qualitative inquiry. Qualitative research has proved important in many areas of clinical and health research, including understanding patients ’ and clinicians ’ decision making and enhancing quality of health services delivery related to utilization, feasibility and appropriate-ness of care.1,2 Despite being on the rise, the publication of qualitative studies in medical journals is still low,3 especially in high-impact journals.4 This pattern is of concern given the role that high-impact journals have in disseminating new evidence to academic and clinical audiences5 as well as to the public through knowledge translation activities that follow publication, including both traditional (newspaper, television and radio) and social (Twitter, blogs) media outlets. Obesity research is not immune to this tendency. Recently, we completed an online search of original manuscripts published from January 2012 to December 2014 in five obesity journals (Childhood Obesity, Clinical Obesity, Interna-
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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 | 0.034 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".