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Record W1606733356

A SURVEY OF PARENTAL BARRIERS TO USING PAIN-REDUCTION STRATEGIES DURING CHILDHOOD VACCINATIONS

2015· article· en· W1606733356 on OpenAlexaffvenue
Alex Zhao, Renata Leong, William Watson

Bibliographic record

VenueUBC Faculty of Medicine medical journal · 2015
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAnxietyPhobiasPerceptionHealth careFamily medicineNursingPsychiatryPsychology
DOInot available

Abstract

fetched live from OpenAlex

Objective: Childhood immunizations represent the most significant source of iatrogenic pain in otherwise healthy children. Consequently, children make correlations between the doctor’s office and the anticipated pain from immunizations and these have long-term consequences such as procedural anxiety, needle phobias, and non-compliance with immunization schedules. Clinical guidelines exist for reducing pain during childhood immunizations. Our study analyzed the use of pain reduction strategies and assessed the barriers that parents face in a family practice setting. Methods: We surveyed parents at academic family practice units at St. Michael’s Hospital. A survey was developed based on a literature search and utilizing current pain reduction guidelines. Results: 62 surveys were recorded and most parents were moderately concerned about their child’s pain. A minority of parents had experience with any of the strategies and the major barriers related to a lack of knowledge and perceptions that pain is a normal part of the immunization experience. Conclusions: We report multiple barriers that parents face when utilizing pain reduction strategies during immunizations. While knowledge, perceptions about pain, and time represent major barriers, healthcare providers should take responsibility in playing an active role in advocating for children while working together with parents.

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)0.007
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.353
Teacher spread0.302 · 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.

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

Citations1
Published2015
Admission routes2
Has abstractyes

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