Black boxes and information pathways: An actor-network theory approach to breast cancer survivorship care
Bibliographic record
Abstract
Many women diagnosed with breast cancer today can expect to live long after completing their treatment. This growing population of survivors encounters distinct post-treatment health and information needs. Existing survivorship care models take information as a given, black boxing it. I use Actor-Network Theory to examine how information actually works for women after they complete breast cancer treatment, and how it shapes their understanding of survivorship. I draw on in-depth interviews with breast cancer survivors (n = 82) and a wide range of providers (n = 84) in a medically underserved region of Southern California. Black boxes and information pathways convey experiential dimensions of cancer care; they are also metaphoric constructs. The black box metaphor refers to the cancer experience as a container; the pathways metaphor refers to a journey. Each of these metaphors expresses salient dimensions of the cancer experience and has implications for post-treatment survivorship. When healthcare information flows smoothly and invisibly, its pathways become black boxed. Black boxes can be helpful when they function effectively. But since black boxes conceal their inner workings, it is challenging to intervene when difficulties arise. I provide three examples of difficulties that complicate women's transition to post-treatment survivorship: (1) when survivors fail to recognize treatment-related late effects, (2) do not understand they have a terminal diagnosis, or (3) worry that their treatment accomplished nothing. Contextualized within survivorship scholarship, this study recommends opening black boxes to examine how information pathways could connect women differently to improve survivorship care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".