You only work with what you know: Healthcare providers’ experiences using non-pharmacological interventions in managing sickle cell crisis pain in adolescents
Bibliographic record
Abstract
Sickle cell crises are the leading reason for hospitalization in adolescents with sickle cell disease. Non-pharmacological interventions are recommended for sickle cell crisis pain management in high and low-resource settings. Preliminary evidence suggests high use of non-pharmacological interventions among adolescents with sickle cell disease in home settings in Nigeria, including potentially harmful ones, and limited use in hospitals, possibly due to healthcare providers' reluctance to use them. Using an interpretive description design, this study explored healthcare providers' perceptions of non-pharmacological interventions and their experience using and recommending these interventions for managing sickle cell crisis pain in adolescents in Nigeria. Individual, semi-structured interviews were conducted. Data were analyzed using constant comparison analysis. Fourteen healthcare providers were recruited from healthcare settings across Nigeria. Five broad themes were identified: 1) Perceptions, 2) Safety and risks of non-pharmacological interventions, 3) Recommended non-pharmacological interventions, 4) Influencing factors, and 5) Non-pharmacological intervention education. Some providers held misconceptions about non-pharmacological interventions, such as believing they were only effective for patients faking pain. These interventions may be recommended for pain management, depending on the provider. The use of questionable interventions, like herbal concoctions, poses a significant health challenge with serious consequences, including death. Identified barriers to the implementation of non-pharmacological interventions include providers' inadequate knowledge about these interventions. The patients' desire to use safe, effective interventions should be supported. Developing contextually relevant educational resources on these interventions might equip providers to support their use during hospitalization, potentially improving their pain outcomes and limiting the use of harmful interventions. PERSPECTIVE: This article presents a qualitative summary of the experiences of healthcare providers in recommending and using non-pharmacological interventions for pain management. This information can guide clinicians and researchers interested in developing educational resources on how these interventions are used, providers' educational needs in Nigeria and dissemination avenues for these resources.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".