Evaluation éthique en psychologie : une utilisation respectueuse des différents instruments psychométriques

Trois psychologues de l'Université de Lausanne, Sophie Perdrix, Linda Charvoz et Jérôme Rossier, abordent dans leur article la relation complexe que le psychologue entretient avec les évaluations psychologiques. Ils plaident en faveur d'une utilisation respectueuse des différences individuelles des instruments d'évaluation et mettent en garde contre leurs aspects réductionnistes.

Psychometric characteristics of the Spanish version of instruments to measure neck pain disability

Background: The NDI, COM and NPQ are evaluation instruments for disability due to NP. There was no Spanish version of NDI or COM for which psychometric characteristics were known. The objectives of this study were to translate and culturally adapt the Spanish version of the Neck Disability Index Questionnaire (NDI), and the Core Outcome Measure (COM), to validate its use in Spanish speaking patients with non-specific neck pain (NP), and to compare their psychometric characteristics with those of the Spanish version of the Northwick Pain Questionnaire (NPQ).Methods: Translation/re-translation of the English versions of the NDI and the COM was done blindly and independently by a multidisciplinary team. The study was done in 9 primary care Centers and 12 specialty services from 9 regions in Spain, with 221 acute, subacute and chronic patients who visited their physician for NP: 54 in the pilot phase and 167 in the validation phase. Neck pain (VAS), referred pain (VAS), disability (NDI, COM and NPQ), catastrophizing (CSQ) and quality of life (SF-12) were measured on their first visit and 14 days later. Patients' self-assessment was used as the external criterion for pain and disability. In the pilot phase, patients' understanding of each item in the NDI and COM was assessed, and on day 1 test-retest reliability was estimated by giving a second NDI and COM in which the name of the questionnaires and the order of the items had been changed.Results: Comprehensibility of NDI and COM were good. Minutes needed to fill out the questionnaires [median, (P25, P75)]: NDI. 4 (2.2, 10.0), COM: 2.1 (1.0, 4.9). Reliability: [ICC, (95%CI)]: NDI: 0.88 (0.80, 0.93). COM: 0.85 (0.75,0.91). Sensitivity to change: Effect size for patients having worsened, not changed and improved between days 1 and 15, according to the external criterion for disability: NDI: -0.24, 0.15, 0.66; NPQ: -0.14, 0.06, 0.67; COM: 0.05, 0.19, 0.92. Validity: Results of NDI, NPQ and COM were consistent with the external criterion for disability, whereas only those from NDI were consistent with the one for pain. Correlations with VAS, CSQ and SF-12 were similar for NDI and NPQ (absolute values between 0.36 and 0.50 on day 1, between 0.38 and 0.70 on day 15), and slightly lower for COM (between 0.36 and 0.48 on day 1, and between 0.33 and 0.61 on day 15). Correlation between NDI and NPQ: r = 0.84 on day 1, r = 0.91 on day 15. Correlation between COM and NPQ: r = 0.63 on day 1, r = 0.71 on day 15.Conclusion: Although most psychometric characteristics of NDI, NPQ and COM are similar, those from the latter one are worse and its use may lead to patients' evolution seeming more positive than it actually is. NDI seems to be the best instrument for measuring NP-related disability, since its results are the most consistent with patient's assessment of their own clinical status and evolution. It takes two more minutes to answer the NDI than to answer the COM, but it can be reliably filled out by the patient without assistance.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Forest owners' acceptance of incentive based policy instruments in forest biodiversity conservation : a choice experiment based approach

Abstract

Summer ozone cycles in the Lisbon metropolitan area and its relation to some meteorological variables

The aim of this work is to study the tropospheric ozone concentrations and daily peak cycles in the Lisbon MetropolitanArea (LMA) during the summer season (June, July and August, JJA) covering the 4-yr study period 2002-2005. Theresults show that all the stations have the same pattern: a minimum in the early morning followed by an increase at 1000UTC reaching to a peak at 1300-1400 UTC, dropped again to minimum values 1800 UTC but with different concentrationsdue to regional and local wind circulations and complex dynamic interactions. We identified in Lisbon city the ozone “weekendeffect”. Finally, we studied an episode of very high levels of tropospheric ozone and related daily ozone concentrationswith some meteorological variables.

Instruments for investigating fitness to drive - needs and expectations in primary care: a qualitative study

Background: Primary care physicians are often requested to assess their patients' fitness to drive. Little is however known on their needs to help them in this task. Aims: The aim of this study is to develop theories on needs, expectations, and barriers for clinical instruments helping physicians assess fitness to drive in primary care. Methods: This qualitative study used semi-structured interviews to investigate needs and expectations for instruments used to assess fitness to drive. From August 2011 to April 2013, we recorded opinions from five experts in traffic medicine, five primary care physicians, and five senior drivers. All interviews were integrally transcribed. Two independent researchers extracted, coded, and stratified categories relying on multi-grounded theory. All participants validated the final scheme. Results: Our theory suggests that for an instruments assessing fitness to drive to be implemented in primary care, it need to contribute to the decisional process. This requires at least five conditions: 1) it needs to reduce the range of uncertainty, 2) it needs to be adapted to local resources and possibilities, 3) it needs to be accepted by patients, 4) choices of tasks need to adaptable to clinical conditions, 5) and interpretation of results need to remain dependant of each patient's context. Discussion and conclusions: Most existing instruments assessing fitness to drive are not designed for primary care settings. Future instruments should also aim to support patient-centred dialogue, help anticipate driving cessation, and offer patients the opportunity to freely take their own decision on driving cessation as often as possible.