Bibliographic record
Abstract
Several years ago I had a conversation with Leora Kuttner, child clinical psychologist and author of the seminal works The Child in Pain (Kuttner, 1996) and No fears, No Tears: Children Coping with Cancer (Kuttner, 1986). This conversation changed my thinking and the way I speak about what it is that we do as clinicians to help people in pain. During our conversation, Dr. Kuttner challenged my use of the term non-pharmacological when referring to cognitive and behavioural interventions to relieve pain. She said the term indicated a bias towards pharmacological interventions and implied that cognitive and behavioural interventions were inferior. Since that conversation, I have tried to be meticulous in my choice of words when describing interventions to relieve pain in infants and children. Although the language becomes cumbersome at times, I have tried to avoid the term non-pharmacological when I really mean behavioural and environmental interventions. I try to avoid implying that pharmacological interventions are the gold standard for pain relief and that we must choose one kind of intervention over the other. I have argued that environmental and behavioural strategies provide the foundational substrate for neonatal pain management to which pharmacological therapy is additive or synergistic (Franck & Lawhon, 1998).
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.016 | 0.035 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".