Caught between hostile and hospitable: Navigating the challenging menopausal journey
Bibliographic record
Abstract
Consumer learning is often associated with positive experiences in marketing literature, where individuals voluntarily engage in acquiring a new consumption repertoire through market resources. In contrast, recent research has shown that learning can also occur in challenging scenarios, where individuals are forced to engage in an uncomfortable learning process, highlighting a clear division between hospitable and hostile environments. However, how do consumers learn in environments that offer both hostile and hospitable experiences? By studying the current menopause context in Canada, I investigate how individuals undergoing this physiological transition cope with uncomfortable bodily changes while engaging with a market that offers various learning resources. Drawing on in-depth interviews, passive netnography, archival data from Reddit forums, YouTube videos and podcasts, my findings reveal that complex vocabulary, idiosyncratic symptoms, diagnostic inaccuracy, and trial-and-error cycles shape the hostile menopause learning environment. Notably, in this environment, institutional resources from relevant health market actors emerge, offering both hospitable and hostile experiences to menopausal individuals. A similar duality is also observed among menopausal communities, where consumers connect to share experiences, seeking mutual support and additional learning resources. This research contributes to the literature on consumer learning, communities and market actors. From a practical perspective, the study also highlights issues in the current health care market and offers insights for both market actors and individuals navigating menopause.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".