Girls like pink: explaining sex-typed occupational aspirations amongst young children

There is a high degree of sex-typing in young children's occupational aspirations and this has consequences for subsequent occupational segregation. Sociologists typically attribute early sex-differences in occupational preferences to gender socialization. Yet we still know surprisingly little about the mechanisms involved in the intergenerational transmission of sex-typical preferences and there is considerable theoretical controversy regarding the role of individual agency in the process of preference formation. This study analyzes the determinants of sex-typed occupational aspirations amongst British children aged between 11 and 15. We specify different mechanisms involved in the transmission of sex-typical preferences and propose an innovative definition of individual agency that is anchored in observable psychological traits linked to self-direction. This allows us to perform a simultaneous test of socialization and agency predictors of occupational sex-typing. We find that parental influences on occupational preferences operate mainly through three distinctive channels: 1) the effect that parental socio-economic resources have on the scope of children's occupational aspirations, 2) children's direct imitation of parental occupations, and 3) children's learning of sex-typed roles via the observation of parental behavior. We also find a strong net effect of children's own psychological predispositions -self-esteem in particular- on the incidence of sex-typical occupational preferences. Yet large differences in the occupational aspirations of girls and boys remain unexplained.

Assessments by Community Occupational Therapists in Northern Ireland - 30 September 2006 (PDF 180 KB)

Presents information on referrals for assessment, waiting times for assessment, and assessments commenced during the quarter.

Review of Occupational Health Services in the HPSS (PDF 387 KB)

Supporting a Healthy Workforce

Occupational exposure of truck drivers to dust and polynuclear aromatic hydrocarbons: a pilot study in Geneva, Switzerland.

The exposure to dust and polynuclear aromatic hydrocarbons (PAH) of 15 truck drivers from Geneva, Switzerland, was measured. The drivers were divided between "long-distance" drivers and "local" drivers and between smokers and nonsmokers and were compared with a control group of 6 office workers who were also divided into smokers and nonsmokers. Dust was measured on 1 workday both by a direct-reading instrument and by sampling. The local drivers showed higher exposure to dust (0.3 mg/m3) and PAH than the long-distance drivers (0.1 mg/m3), who showed no difference with the control group. This observation may be due to the fact that the local drivers spend more time in more polluted areas, such as streets with heavy traffic and construction sites, than do the long-distance drivers. Smoking does not influence exposure to dust and PAH of professional truck drivers, as measured in this study, probably because the ventilation rate of the truck cabins is relatively high even during cold days (11-15 r/h). The distribution of dust concentrations was shown in some cases to be quite different from the expected log-normal distribution. The contribution of diesel exhaust to these exposures could not be estimated since no specific tracer was used. However, the relatively low level of dust exposure dose not support the hypothesis that present day levels of diesel exhaust particulates play a significant role in the excess occurrence of lung cancer observed in professional truck drivers.

Occupational Therapy Review January 2004 (PDF 55 KB)

Workforce Planning Review

Occupational Therapy Review March 2003 (PDF 350 KB)

Workforce Planning Review

Comprehensive Review of the HPSS Occupational Therapy Workforce (PDF 429 KB)

Report of the Project Group

Solid phase microextraction as a short-term sampling technique for BTEX occupational exposure

Solid phase microextraction (SPME) has been widely used for many years in various applications, such as environmental and water samples, food and fragrance analysis, or biological fluids. The aim of this study was to suggest the SPME method as an alternative to conventional techniques used in the evaluation of worker exposure to benzene, toluene, ethylbenzene, and xylene (BTEX). Polymethylsiloxane-carboxen (PDMS/CAR) showed as the most effective stationary phase material for sorbing BTEX among other materials (polyacrylate, PDMS, PDMS/divinylbenzene, Carbowax/divinylbenzene). Various experimental conditions were studied to apply SPME to BTEX quantitation in field situations. The uptake rate of the selected fiber (75 microm PDMS/CAR) was determined for each analyte at various concentrations, relative humidities, and airflow velocities from static (calm air) to dynamic (> 200 cm/s) conditions. The SPME method also was compared with the National Institute of Occupational Safety and Health method 1501. Unlike the latter, the SPME approach fulfills the new requirement for the threshold limit value-short term exposure limit (TLV-STEL) of 2.5 ppm for benzene (8 mg/m(3))

An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.