Mediating health information : the go-betweens in a changing socio-technical landscape
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CHAPTER 1: The Go-Betweens: Health, Technology and Info(r)mediation. Sally Wyatt, Roma Harris and, Nadine Wathen CHAPTER 2: 'Everybody's Talking at Me': Situating the Client in the Info(r)mediary Work of the Health Professions. Leslie Bella, Roma Harris, Debbie Chavez, Jana Fear and Penny Gill CHAPTER 3: Health Intermediaries? Positioning the Public Library in E-Health Discourse. Flis Henwood, Roma Harris, Samantha Burdett and Audrey Marshal CHAPTER 4: To Filter or Not to Filter: Legal and Ethical Aspects of Librarians' Use of Internet Filtering Techniques. Elaine Gibson and Jan Sutherland CHAPTER 5: Invisible Logic: The Role of Software as an Information Intermediary in Health Care. Ellen Balka and Arsalan Butt CHAPTER 6: Personalized Narrative Diagnostic Imaging: Can it Mediate Patient-System Dialogue? Peter Pennefather and West Suhanic CHAPTER 7: Using the Internet as a Health Intermediary: Providing Information and Services to Marginalized Sexual Communities. T.C. Sanders CHAPTER 8: Between the Clinic and the Community: Pathways for an Emerging E-Health Policy in the Remote First Nations of Northwestern Ontario. Adam Fiser and Robert Luke CHAPTER 9: We're All Out there Busting Our Guts, Trying to Do the Best that We Can for Our People': Health Intermediaries in the Australian Indigenous Communities. Lyn Simpson, Michelle Hall and Susan Leggett CHAPTER 10: Helpers, Gatekeepers and the Well-Intentioned: The Mixed Blessings of HIV/AIDS Info(r)mediation in Rural Canada. Roma Harris, Tiffany Veinot, Leslie Bella, Irving Rootman and Judith Krajnak CHAPTER 11: Reflections on the Middle Space. Nadine Wathen, Roma Harris and Sally Wyatt.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| 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 it