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Record W3037952675 · doi:10.1080/24740527.2020.1783524

Parents’ management of adolescent patients’ postoperative pain after discharge: A qualitative study

2020· article· en· W3037952675 on OpenAlexaff
William Dagg, Paula Forgeron, Gail Macartney, Julie Chartrand

Bibliographic record

VenueCanadian Journal of Pain · 2020
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Prince Edward IslandUniversity of Ottawa
Fundersnot available
KeywordsMedicinePsychological interventionHospital dischargePain managementDischarge planningTelephone interviewPostoperative painQualitative researchInterpretative phenomenological analysisPhysical therapyNursingAnesthesiaIntensive care medicine

Abstract

fetched live from OpenAlex

Background: Short hospital admission periods following pediatric inpatient surgery leave parents responsible for managing their child’s postoperative pain in the community following discharge. Little is known about the experiences of parents caring for their child’s postoperative pain after discharge home following inpatient surgery. Research examining parental postoperative pain management following their child’s day surgery has found that parents are challenged in their pain management knowledge and practices.Aims: This interpretative phenomenological analysis study sought to understand parents’ experiences caring for their child’s postoperative pain at home.Methods: Semistructured telephone interviews were conducted with seven parents between 2 weeks and 6 months after their child’s discharge from hospital.Results: Identified themes were coming home without support, managing significant pain at home, and changes in the parent–child relationship.Conclusions: Parents could potentially benefit from nurses optimizing educational interventions, from receiving ongoing support of transitional pain teams, and from assistance with return to school planning.

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 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)0.003
metaresearch head score (Gemma)0.001
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 score0.157
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.293
Teacher spread0.269 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2020
Admission routes1
Has abstractyes

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