Mediating health information : the go-betweens in a changing socio-technical landscape
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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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.023 | 0.085 |
| Scholarly communication | 0.043 | 0.049 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".