Bibliographic record
Abstract
there a significant difference in the nurses’ attribution of pain to each of the two ethnic groups? (3) Was there a significant difference in the patients’ and the nurses’ evaluations of patient pain? Using the McGill Pain Questionnaire, amount of analgesia and three physiologic measures, no differences were found between the two ethnic groups on any of the measures of pain. The nurses’ assessment of pain was measured using the Present Pain Intensity Scale; nurses assigned more pain to Anglo patients than to Mexican American patients. Comparing patients’ and nurses’ evaluation of pain, all nurses evaluated patients’ pain as less than the patients did. Examining other sample characteristics, for the nurses, pain was significantly related to patient education, place of birth, language, and religion (Calvillo & Flaskerud, 1993). This study highlighted the need for nurses to be more aware of their own values and perceptions, as these may affect how they evaluate patient’s pain and how that pain is treated or managed. The study also supported the frequently reported finding that health professionals underestimate or even discount patient pain. There have been many developments in the assessment of pain since this study was published 20 years ago. Criticism of health professionals’ disregard for patient pain has led to mandated assessments of pain. Patient pain is evaluated routinely now when a person comes for an outpatient clinical visit or when in the hospital. The Joint Commission on Accreditation of Healthcare Organizations (JCAHO) requires that accredited hospitals and clinics must routinely assess all patients for pain and the Veterans Administration has designated pain as the 5th vital sign (Krebs, Carey, & Weinberger, 2007; Mularski et al., 2006). The practice of universal pain screening has become widespread and a commonly used measure is the Numerical Rating Scale (NRS) to screen for pain. All of us have been asked to rate our level of pain on a scale of 0 (no pain) to 10 (worst possible pain) and to mark on a front and back drawing of a person where our pain is occurring. Despite this assessment being better than nothing, its subjective, imprecise and repetitive nature makes it easy for healthcare workers to ignore or dismiss. It also can be difficult for patients to communicate their pain using the NRS, or conversely, it has been easy to abuse in emergency rooms for patients seeking narcotic prescriptions.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".