From individual coping strategies to illness codification: the reflection of gender in social science research on Multiple Chemical Sensitivities (MCS)
Bibliographic record
Abstract
INTRODUCTION: Emerging fields such as environmental health have been challenged, in recent years, to answer the growing methodological calls for a finer integration of sex and gender in health-related research and policy-making. METHODS: Through a descriptive examination of 25 peer-reviewed social science papers published between 1996 and 2011, we explore, by examining methodological designs and theoretical standpoints, how the social sciences have integrated gender sensitivity in empirical work on Multiple Chemical Sensitivities (MCS). MCS is a "diagnosis" associated with sensitivities to chronic and low-dose chemical exposures, which remains contested in both the medical and institutional arenas, and is reported to disproportionately affect women. RESULTS: We highlighted important differences between papers that did integrate a gender lens and those that did not. These included characteristics of the authorship, purposes, theoretical frameworks and methodological designs of the studies. Reviewed papers that integrated gender tended to focus on the gender roles and identity of women suffering from MCS, emphasizing personal strategies of adaptation. More generally, terminological confusions in the use of sex and gender language and concepts, such as a conflation of women and gender, were observed. Although some men were included in most of the study samples reviewed, specific data relating to men was undereported in results and only one paper discussed issues specifically experienced by men suffering from MCS. Papers that overlooked gender dimensions generally addressed more systemic social issues such as the dynamics of expertise and the medical codification of MCS, from more consistently outlined theoretical frameworks. Results highlight the place for a critical, systematic and reflexive problematization of gender and for the development of methodological and theoretical tools on how to integrate gender in research designs when looking at both micro and macro social dimensions of environmental health conditions. CONCLUSIONS: This paper contributes to a discussion on the methodological and policy implications of taking sex and gender into account appropriately in order to contribute to better equity in health, especially where the critical social contexts of definition and medico-legal recognition play a major role such as in the case of MCS.
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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 | 0.068 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".