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Record W4386989301 · doi:10.1093/pch/pxad055.075

75 Factors that Influence Parental Decision-making Regarding Analgesia for their Children with Musculoskeletal Injury-related Pain: A Qualitative Study

2023· article· en· W4386989301 on OpenAlexaboutno aff
Manisha Bharadia, Zoë Dworsky-Fried, Mackenzie Moir, Manasi Rajagopal, Serge Gouin, Scott Sawyer, Stéphanie Pellerin, Lise Bourrier, Naveen Poonai, Antonia Stang, Michael van Manen, Samina Ali

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHydromorphoneThematic analysisEmergency departmentQualitative researchPain managementFocus groupPhysical therapyOpioidNursing

Abstract

fetched live from OpenAlex

Abstract Background Parents/caregivers are often the gatekeepers for the pharmacologic management of their children’s pain. Parents’ unique expertise in assessing their children’s pain reactions, alongside a deep emotional drive to protect their children, make them invaluable advocates and partners. Parents are highly invested in optimizing pain management outcomes for children. Concerns and preferences regarding medications, including opioids, influence their choices. An improved understanding of caregiver decision-making should encourage a family-centred approach to communication with parents when deciding analgesic plans for children with acute pain, facilitate shared clinical decision-making, and optimize paediatric pain management outcomes in the clinical setting. Objectives Our primary objective was to explore and understand caregiver decision-making as it relates to acute pain management for children presenting to the emergency department, with particular focus on opioids. Design/Methods This qualitative study was embedded within an ongoing paediatric clinical trial (‘The No OUCH Trials’, NCT03767933), which aims to evaluate the clinical efficacy of a combination of oral opioid (hydromorphone) and non-opioid (ibuprofen and acetaminophen) analgesics to manage paediatric musculoskeletal injury-related pain. This study employed one-on-one semi-structured interviews. Parents of children with acute musculoskeletal injuries were recruited from three Canadian paediatric emergency departments (Stollery Children’s Hospital [Edmonton, Alta.], CHU Sainte-Justine [Montreal, Qué.], and Winnipeg Children’s Hospital [Winnipeg, Man.]). Interviews were conducted via telephone from June 2019 to March 2021. Verbatim transcription and thematic analyses occurred concurrently with data collection, supporting data saturation and theory development considerations. Results Twenty-seven interviews were completed. Five major themes regarding pain assessment and treatment emerged: a) My child’s comfort is a priority; b) Every situation is unique; c) Opioids only if necessary; d) Considerations when choosing opioids; and e) Pain research is important. Overall, parents were highly comfortable with their assessment of their child’s pain. Participants’ willingness to use opioid analgesia for their children was primarily dependent on perceptions of injury and pain severity. Although considerations for opioid use were similar between opioid-averse and opioid-willing families, the trade-offs between maximizing pain relief and minimizing risks were weighed differently. Conclusion Our study revealed that parents assess their child’s pain and distress as a global entity, with great confidence in their own assessment and decision-making. For most parents, the desire to relieve their children’s pain outweighed concerns of addiction, misuse, and adverse events when making decisions about opioid analgesia for short-term use. These results can inform evidence-based family-centred approaches to co-decision-making of analgesic plans for children with acute pain.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.357
Teacher spread0.337 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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