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CONTINUING HEALTH EDUCATION IN THE QUALIFICATION OF PROFESSIONALS FOR HEALTH TECHNOLOGY ASSESSMENT: INTEGRATIVE REVIEW
No full textHealth Technology Assessment (HTA) is a strategic tool for supporting evidence-based decision-making in health systems and requires qualified professionals for its proper application. However, undergraduate health education still presents gaps in HTA-related training, making Continuing Health Education (CHE) an important strategy for developing competencies in this field. This study aims to conduct an integrative literature review on continuing health education as a strategy for professional qualification in Health Technology Assessment. This integrative review follows the methodological steps proposed by Whittemore and Knafl and the recommendations of the Joanna Briggs Institute. The literature search was conducted in MEDLINE via PubMed, LILACS, and SciELO databases using MeSH and DeCS descriptors combined with Boolean operators. Studies addressing continuing education initiatives focused on HTA training for health professionals were included. The findings of this review are expected to identify educational strategies, competencies developed, and the impacts of training actions on professional practice and health decision-making, supporting the planning of capacity-building programs and strengthening HTA implementation within health services
Bibliografía. Elaboración participativa de Procedimientos Normalizados de trabajo para la movilización precoz con silla verticalizadora y grúa bipedestadora en UCI y hospitalización convencional.
No full textBibliografía completa póster
Environmental Impact of Australian Diets Scoping Review
No full textThis scoping review has been commissioned by NHMRC as part of a revision to the 2013 Australian Dietary Guidelines.
The aim of this scoping review is to identify the available evidence on the environmental impacts of consuming different dietary patterns, protein-rich foods and ultra-processed foods (UPFs), in the Australian context. The review will explore the study characteristics and findings, synthesise summary evidence, and identify evidence gaps in Australian research
S.M.A.R.T (mathematical framework and technical verification)
No full textThis is the mathematical and technical framework to back up the smart theor
Evaluating the quality of brainstem ROI registration using structural and diffusion MRI - Dataset
No full text
Financial_choices_and_advice_v1
No full textThis is a second update to the first registration. For some reason, if I click "Update" on the first registration, it does not allow me to upload files. Data collection has not yet begun. This update reflects changes to the lab setup (single room with alternating batches instead of two simultaneous rooms), batch scheduling procedure, compensation (base payment updated to £15), addition of a 17th bias (T — Default Effect, bringing total trials to 27), minor prompt wording refinements, and documentation of standardised informational aids (Important Box and Short Glossary) shown to all participants.
Description:
This lab experiment tests whether access to a GPT-based AI advisor affects behavioral biases in financial decisions. Participants (N=120) are run in a single room in batches of 15, alternating between GPT (AI chat available) and NO_GPT (no AI) conditions. Each participant completes 27 trials measuring 17 cognitive biases (e.g., loss aversion, framing effect, anchoring, sunk cost fallacy, base rate neglect).
Research Question: Does GPT assistance reduce or amplify biases?
Hypothesis: Non-directional — we test whether behavior differs between groups without predicting direction.
Primary Analysis: Compare GPT vs NO_GPT responses for each bias using chi-square, t-tests, ANOVA, and logistic regression as appropriate.
Secondary Analysis: Per-protocol analysis (GPT participants who actually used the AI), FDR-adjusted p-values for multiple comparisons, and within-person contrasts for mixed-design biases.
Exploratory Analysis: GPT usage patterns, demographic moderators of GPT effects, and composite bias scores.
Ethics: LBS REC108
Safety Through Incapability: Benchmarking an Intentionally Overfitted Medical Language Model Against Frontier LLMs on Consumer Health Query Safety
No full textThis study evaluates the safety of consumer-facing AI responses to medical queries by benchmarking
PlaceboGPT — an intentionally overfitted 7,666-parameter language model that returns a single safe response to all inputs — against four frontier large language models (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3 70B). Using a stratified sample of 500 queries from the HealthSearchQA dataset and an LLM-as-a-Judge evaluation framework, we will assess each model's responses across seven safety dimensions and three helpfulness dimensions. The study aims to quantify the safety–helpfulness tradeoff in consumer medical AI and to propose intentional overfitting as a formal safety baseline for the field
