Negotiating What Constitutes Depression: Focus Group Conversations in Response to Viewing Direct-to-Consumer Advertisements for Antidepressants
Bibliographic record
Abstract
Background Direct-to-consumer (DTC) advertisements for medication communicate a distinct image of illness and have the potential to shape how we understand what constitutes illness.Analysis The purpose of this study was to explore discursive patterns in how women interact with the messages related to depression in DTC television advertisements for antidepressants. We conducted six focus groups of 1 to 2 hours, with 4 to 6 female participants per group. Within each group, participants viewed and discussed 2 to 3 DTC advertisements.Conclusions and implications Using discourse analysis to explore how the women engaged with the messages in the advertisements, we show how participants reclaimed what constitutes “normal” and “depression” and often positioned the ads as falling short in their presentations of these categories.Keywords Antidepressants; Discourse analysis; Advertisement; DepressionContexte La publicité directe au consommateur (PDC) sur les médicaments véhicule une image particulière de la maladie qui peut infléchir notre avis sur ce qu’est celle-ci.Analyse L’objectif de cette étude était d’explorer des structures discursives relatives à la manière dont les femmes perçoivent les messages sur la dépression communiqués par des PDC sur les antidépresseurs. Pour ce faire, nous avons mené six groupes de discussion d’une à deux heures comptant 4 à 6 femmes par groupe. Au sein de chaque groupe, les participantes ont regardé et commenté 2 ou 3 PDC.Conclusion et implications Nous avons effectué une analyse du discours afin d’explorer la manière dont les femmes interprètent les messages des PDC. Nous montrons comment les participantes se sont accordées sur le sens de « normal » et de « dépression » tout en percevant les PDC comme inadéquats dans leur présentation de ces concepts.Mots clés Antidépresseurs; Analyse du discours; Publicité; Dépression
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".