A New Approach to Eliciting Meaning in the Context of Breast Cancer
Bibliographic record
Abstract
A semistructured measure was developed from early descriptive work by Lipowski to elicit the meaning of breast cancer using eight preset categories: challenge, enemy, punishment, weakness, relief, strategy, irreparable loss, and value. This measure was applied in two studies: a cross-sectional survey of 1012 Canadian women at various points after diagnosis and a follow-up study 3 years later of 205 women from the previous study who were close to the time of diagnosis at the first testing. The majority of the 1012 women chose "challenge" (57.4%) or "value" (27.6%) to describe the meaning of breast cancer, whereas fewer chose the more negative "enemy" (7.8%) or "irreparable loss" (3.9%). At the 3-year follow-up assessment, 78.9% of the women who had indicated positive meaning by their choices of "challenge" or "value" did so again. Verbal descriptions provided by the women were congruent with those reported in previous qualitative studies of meaning in breast cancer with respect to the two most prevalent categories: challenge and value. At follow-up assessment, women who ascribed a negative meaning of illness with choices such as "enemy," "loss," or "punishment" had significantly higher levels of depression and anxiety and poorer quality of life than women who indicated a more positive meaning. The meaning-of-illness measure provides an approach that can be applied in large surveys to detect women who ascribe less positive meaning to the breast cancer experience, women who may be difficult to identify in the context of small, qualitative studies.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".