Pain Management in the Context of a Nursing Help Line: Identification of Needs, Development of a Continuing Education Activity, and Evaluation of its Impact
Bibliographic record
Abstract
Introduction: Pain management by nurses in the context of a phone help line is a complex task. Continuing education (CE) is a potentially effective strategy to ensure development of this competence. Objective: The main objective of this study was to develop and evaluate a customized pain management CE activity by and for nurses working at a phone help line providing health information. Methods: A three-phase convergent mixed-method design was used: needs and preferred educational strategies assessment, conception of CE activity, evaluation. Based on a participatory approach, the CE activity was developed to meet participants’ expectations and needs. It included two components: 1) CE day and 2) individual clinical support. A quasi-experimental study with a single time series was used to evaluate the CE activity regarding participants’ knowledge and beliefs about pain management and their perceptions of their pain management activities. Data collection was performed using focus groups and questionnaires. Results: Participants’ knowledge about pain management increased after the CE day and remained stable after three months. Also, participants reported an increase in various patient-centered pain management nursing activities. Discussion and conclusion: This study illustrates the importance of involving nurses in designing a CE activity and supports its potential benefits in the context of a phone help line.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".