Demographic and clinical characteristics of free-text writers in chronic pain patient intake questionnaires
Bibliographic record
Abstract
Background Chronic pain is a prevalent and burdensome problem within the Canadian health care system, where the gold standard treatment occurs at multidisciplinary pain facilities. Patient intake questionnaires (PIQs) are standard practice for obtaining health information, with many patients including free-text (e.g., writing in margins of questionnaires) on their PIQs.Aims This study aims to quantitatively examine whether and how patients who include free-text on PIQs differ from those who do not.Methods We retrospectively analyzed 367 PIQs at a Canadian pain facility in Winnipeg, Canada. Patients were categorized into free-text (i.e., any text response not required in responding to questions) or no free-text groups. Groups were compared on sociodemographics, pain, health care utilization, and depressive symptoms with independent samples t-tests and chi-square analyses.Results Patients with free-text compared to those without had more sources of pain (6.66 vs. 4.63), longer duration of pain (123.2 months vs. 68.1 months), and a greater proportion of past pain conditions (66.3% vs. 55.2%). Additionally, they had tried more treatments for their pain, had seen more specialists, had tried more past medications, were currently on more medications, and had undergone more tests. No differences were identified for depressive symptoms across groups.Conclusions This study is the first to examine patient and health-related correlates of free-text on PIQs at a Canadian pain facility. Results indicate that there are significant differences between groups on pain and health care utilization. Thus, patients using free-text may require additional supports and targeted interventions to improve patient–physician communication and patient outcomes.
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 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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".