Bibliographic record
Abstract
Scotland Yard Inspector: "Is there any other point to which you would wish to draw my attention?"Sherlock Holmes: "To the curious incident of the dog in the night-time."Inspector: "The dog did nothing in the night-time."Holmes: "That was the curious incident."A s Sherlock Holmes did in The Adventure of Silver Blaze (Doyle 1893), André Picard, health reporter and columnist at The Globe and Mail, recently drew attention to the importance of reflecting on curious silences.During a January 2018 panel, he encouraged focus on issues that may go unreported or understudied, not just those regularly in the headlines.Every Monday, I am reminded of one such issue.I start the week by volunteering in a program for people who are experiencing chronic pain.Their stories of how being in pain affects their lives are powerful, as is their drive to find a path forward.While the opioid crisis is getting much-needed attention, there is less talk about the rise in the number of Canadians with chronic pain.Statistics Canada data show that 4.9 million Canadians aged 12 and older reported having pain or discomfort that prevented activities in 2014, up from 2.8 million in 2003 (Statistics Canada 2016).That reflects a rise from 10.6% of teens and adults to 14.9%.What' s happening?The change is not explained by population aging.Age-standardized rates are also higher now than in 2003; rates have risen for both women and men.Nor is it because of changes in arthritis rates (17.6% of teens and adults said that they had arthritis in 2003, compared to 16.5% in 2014).Statistics Canada data also rule out injuries as a material explanation since the proportion of people who sought medical attention for injuries over the period was relatively stable.One would have to go further to understand what the true drivers are.Authors in this issue of Healthcare Policy/Politiques de Santé followed their curiosity in pursuit of answers to a broad range of topical questions.Their work uses a variety of methods and approaches to address ethical, healthcare financing, quality, and other issues.As you pursue understanding of the curious healthcare policy silences that have peaked your own interest, please join these authors in submitting high-quality research and debate for publication in the journal' s pages.Whether your work illuminates an important issue Curious Silences in Healthcare Policy and Research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.085 | 0.252 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.067 | 0.089 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".