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Interventions to improve question formulation in professional practice and self-directed learning

2010· review· en· W1869089727 on OpenAlexaff
Tanya Horsley, Jennifer O’Neill, Jessie McGowan, Laure Perrier, Gabrielle Kane, Craig Campbell

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2010
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of TorontoInstitute of Population and Public HealthUniversity of OttawaRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsPsychological interventionHealth careCompetence (human resources)Context (archaeology)Medical educationPsychologyCurriculumMedicineNursingSocial psychologyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Formulating questions is fundamental to the daily life of a healthcare worker and is a defining characteristic of professional competence and meaningful learning. With high expectations for healthcare providers to remain up-to-date with current evidence and the movement towards formalizing reflective practice as part of physician revalidation, it is important that curricula developed for improving the ability to formulate well-constructed questions are based on the best evidence. OBJECTIVES: To assess the effectiveness of interventions for increasing the frequency and quality of questions formulated by healthcare providers in practice and the context of self-directed learning. SEARCH STRATEGY: We obtained studies from searches of electronic bibliographic databases, and supplemented these with handsearching, checking reference lists, and consultation with experts. SELECTION CRITERIA: We considered published and unpublished randomized controlled trials (RCTs), controlled clinical trials (CCTs), interrupted time-series (ITS), and controlled before-after (CBA) studies of any language examining interventions for increasing the quality and frequency of questions formulated by health professionals involved with direct patient care. DATA COLLECTION AND ANALYSIS: Two review authors independently undertook all relevancy screening and 'Risk of bias' assessment in duplicate. Intervention characteristics, follow-up intervals, and measurement outcomes were diverse and precluded quantitative analysis. We have summarized data descriptively. MAIN RESULTS: Searches identified four studies examining interventions to improve question formulation in healthcare professionals. Interventions were mostly multi-component, limited within the context of EBM and primarily in physician and resident populations. We did not identify studies examining changes in frequency of questions formulated or those within the context of reflection. Risk of bias was generally rated to be 'high risk'. Three of the four studies showed improvements in question formulation in physicians, residents, or mixed allied health populations in the short- to moderate term follow up. Only one study examined sustainability of effects at one year and reported that skills had eroded over time. AUTHORS' CONCLUSIONS: Evidence from our review suggests that interventions to increase the quality of questions formulated in practice produce mixed results at both short- (immediately following intervention), and moderate-term follow up (up to nine months), comparatively. Although three studies reported effectiveness estimates of an educational intervention for increasing the quality of question formulation within the short term, only one study examined the effectiveness in the longer term (one year) and revealed that search skills had eroded over time. Data suggests that sustainability of effects from educational interventions for question formulation are unknown.

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)0.057
metaresearch head score (Gemma)0.179
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.057
Threshold uncertainty score0.301

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.179
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0080.006
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.001

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 head0.071
GPT teacher head0.472
Teacher spread0.401 · 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".

Quick stats

Citations39
Published2010
Admission routes1
Has abstractyes

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