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Record W2122324831 · doi:10.1177/0193945906295522

Pain Management Decision Making Among Long-Term Care Physicians and Nurses

2007· article· en· W2122324831 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWestern Journal of Nursing Research · 2007
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of ReginaHamilton Health SciencesMcMaster University
FundersCanadian Institutes of Health Research
KeywordsPain managementMedicineAffect (linguistics)CognitionMEDLINEHealth careLong-term carePain assessmentNursingClinical decision makingHealth professionalsPsychologyFamily medicinePsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

The purpose of this study is to explore attitudes and beliefs that affect decisions about prescribing and administering pain medications in older adults who live in long-term care (LTC), with a particular emphasis on those with cognitive impairment. At each of the four participating LTC facilities, data were gathered from three separate groups of health care professionals: physicians, registered nurses, and registered practical nurses. Based on grounded theory, a model was developed that highlighted critical decision points for nurses and physicians regarding pain management. The major themes that emerged from the data concerned pain assessment (lack of recognition of pain, uncertainty about the accuracy of pain assessment and diagnosis) and treatment (reluctance to use opioids, working to individualize pain treatments, issues relating to physician trust of the nurse on prescribing patterns). These findings may facilitate the development of innovative approaches to pain management in LTC settings.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.433
Teacher spread0.386 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it