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Record W2971695840

Shared Decision Making in Routine Childhood Vaccination: What do Parents Want?

2017· article· en· W2971695840 on OpenAlexaff
Tom Snelling, Lyndal Trevena, Holly O. Witteman, Nina J Berry, Margie Danchin, Paul Kinnersley, Penelope Robinson, Kristine Macartney, Julie Leask

Bibliographic record

VenueUWA Profiles and Research Repository (University of Western Australia) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsVaccinationPsychologyMedicineVirology
DOInot available

Abstract

fetched live from OpenAlex

BackgroundIn order to enhance implementation of shared decision making (SDM) in older patients with multiple chronic conditions (MCC), we conducted a review on barriers and limitations that older patients with MCC, their informal caregivers and health professionals.Methods Medline, Embase, Psycinfo, Cinahl and Cochrane Central Register of Controlled Trials (Central) were searched covering the period from 1980 to April 2016.We used both keywords and MESH terms for 'shared decision making', 'older people', 'comorbidities', 'barriers' and 'facilitators'.Studies were included if they had an original data collection, both qualitative and quantitative studies were included.Two researchers independently reviewed all data, including a quality assessment.Barriers and facilitators were reported according to an existing taxonomy for predisposing factors, interactional context factors, the SDM encounter, organisational, social and policy factors.We described from which perspective (patient, informal caregiver, health professional) which barriers and facilitators were reported. ResultsWe reported only those barriers (b) and facilitators (f) that were not previously reported in reviews about b&f in a general population.Patient reported b & f were mainly about: hectic environment (b), not really allowed to decide (b), bad communication skills professional (b), individual approach (f), forming partnership with professional (f) and feeling invited (f).Health professional reported barriers and facilitators concerned: patient not wanting to participate, thus having to guess (b), organisational constraints (b) and working in a multidisciplinary team (f).Informal caregivers reported barriers and facilitators addressed: having to deal with complex and multiple health organisations (b), stress between own values and interests and patients values and interests (b), being extra eye and ear for both patient as well as health professional (f).The major part of barriers and facilitators was experienced by both patients as professionals. ConclusionsOlder people with MCC, informal caregivers and health professionals caring for them experience particular barriers as facilitators in the SDM process, in addition to general and well known barriers and facilitators.There is consensus between patients and health professionals about most barriers and facilitators, which is a fertile starting point for enhanced implementation of SDM.

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)0.031
metaresearch head score (Gemma)0.142
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.142
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0060.007
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.083
GPT teacher head0.382
Teacher spread0.300 · 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".

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Citations0
Published2017
Admission routes1
Has abstractno

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