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P36 Effectiveness of interventions to improve self-management for adolescents and young adults with allergic conditions: A systematic review

2019· review· en· W2990379377 on OpenAlexaboutno aff
Liz Angier, Cherry Alviani, Charlotte Gotthard Moertz, Marta Vázquez‐Ortiz, Graham Roberts

Bibliographic record

Venuenot available
Typereview
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMotivational interviewingMoodMedicineSystematic reviewAsthmaSelf-managementRandomized controlled trialQuality of life (healthcare)MEDLINECoping (psychology)Physical therapyClinical psychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

Aims This systematic review aimed to review the literature on interventions for improving self-management and wellbeing in adolescents and young adults (11-25 years) with allergic conditions, including asthma. Methods A search strategy was constructed using Cochrane Database of Systematic Reviews, MEDLINE, Embase and Psychinfo. Studies had to be controlled trials or randomised control trials. Databases were searched to February 10, 2019. Quality assessments and data extraction were undertaken independently by two reviewers. Results A total of 30 papers reporting data from 27 studies were included. Studies were conducted in the USA (k=17); Netherlands (k=2); Iran (k=2); Australia (k=2); Jordan (k=1); Canada (k=1); UK (k=1); Germany (k=1). Interventions were of 4 main types: psychological (k=9); E-health (k=8); educational (k=4); peer led (k=5); with 1 intervention focused on breathing re-training. All interventions were for asthma. Psychological interventions used cognitive behavioural or motivational interviewing methods, emotional disclosure, stress management or problem solving to improve health outcomes. Significant improvements in the intervention group compared to the control group were found for self-esteem, quality of life, self-efficacy, coping strategies and mood alongside improvements in asthma symptoms. E-Health interventions included the use of web-based computer tailored information, telecommunication compressed videos or mobile applications. Significant improvements were seen across most studies in inhaler technique, adherence, quality of life and in asking questions about asthma medication, triggers and environmental control. Educational interventions included group sessions focusing on asthma prevention and management, individual coaching sessions and nurse-led asthma clinics. They demonstrated significantly improved management of asthma symptoms, improved controller medication use and increased use of a written management plan, reduction in symptoms and improved quality of life. The peer led interventions included the Triple A (Adolescent Asthma Action) programme and a peer-led camp based on the Power Breathing Programme. Improvements were found for self-efficacy, school absenteeism and quality of life. Conclusion Although significant improvements were seen across all intervention types, many studies were small feasibility or pilot studies and no studies for allergic conditions other than asthma met the inclusion criteria. Large longitudinal interventional studies across the range of allergic conditions are required to strengthen the evidence base.

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)0.011
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.046
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.011
Bibliometrics0.0090.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.059
GPT teacher head0.473
Teacher spread0.414 · 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 designSystematic review
Domainnot available
GenreReview

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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