Profiling adult literacy facilitators in development contexts: An ethnographic study in Ethiopia

Teachers/facilitators in adult literacy learning programmes are recognised as being vital to successful learning outcomes. But little is known about them as a group. This small-scale research project comprising ethnographic-style case studies of five adult literacy facilitators (ALFs) in Ethiopia seeks to throw some light on these teachers, their backgrounds and what they bring to their teaching, with a view to improving the effectiveness of their work. The researchers found that all of the ALFs had high levels of commitment, but none of the ALFs received much in the way of training, and professional support for their role was in some cases missing. The degree (and their perception) of their own literacy practices varied greatly among them, even in their common use of mobile phones. It also emerged that while they had all fought very hard for their own education, one of the main reasons all of them stated for going into literacy teaching was not a general belief in the value of education but their priority need of a regular income. Another insight is that the female ALFs struggled more than their male counterparts in engaging learners; the women were criticised more excessively than the men. This research reveals something of the diversity of facilitators, and concludes that further such studies are needed in different contexts

Characterization of constricted fruit (ctf) Mutant Uncovers a Role for AtMYB117/LOF1 in Ovule and Fruit Development in Arabidopsis thaliana

Pistil and fruit morphogenesis is the result of a complex gene network that is not yet fully understood. A search for novel genes is needed to make a more comprehensive model of pistil and fruit development. Screening for mutants with alterations in fruit morphology generated by an activation tagging strategy resulted in the isolation of the ctf (constricted fruit) mutant. It is characterized by a) small and wrinkled fruits, with an enlarged replum, an amorphous structure of the septum and an irregular distribution of ovules and seeds; b) ectopic carpelloid structures in sepals bearing ovule-like structures and c) dwarf plants with curled rosette leaves. The overexpressed gene in ctf was AtMYB117, also named LOF1 (LATERAL ORGAN FUSION1). AtMYB117/LOF1 transcripts were localized in boundary regions of the vegetative shoot apical meristem and leaf primordia and in a group of cells in the adaxial base of petioles and bracts. Transcripts were also detected in the boundaries between each of the four floral whorls and during pistil development in the inner of the medial ridges, the placenta, the base of the ovule primordia, the epidermis of the developing septum and the outer cell layers of the ovule funiculi. Analysis of changes of expression of pistil-related genes in the ctf mutant showed an enhancement of SHATTERPROOF1 (SHP1) and SHP2 expression. All these results suggest that AtMYB117/LOF1 is recruited by a variety of developmental programs for the establishment of boundary regions, including the development of floral organs and the initiation of ovule outgrowth

Adjusting for multiple prognostic factors in the analysis of randomised trials

Background: When multiple prognostic factors are adjusted for in the analysis of a randomised trial, it is unclear (1) whether it is necessary to account for each of the strata, formed by all combinations of the prognostic factors (stratified analysis), when randomisation has been balanced within each stratum (stratified randomisation), or whether adjusting for the main effects alone will suffice, and (2) the best method of adjustment in terms of type I error rate and power, irrespective of the randomisation method. Methods: We used simulation to (1) determine if a stratified analysis is necessary after stratified randomisation, and (2) to compare different methods of adjustment in terms of power and type I error rate. We considered the following methods of analysis: adjusting for covariates in a regression model, adjusting for each stratum using either fixed or random effects, and Mantel-Haenszel or a stratified Cox model depending on outcome. Results: Stratified analysis is required after stratified randomisation to maintain correct type I error rates when (a) there are strong interactions between prognostic factors, and (b) there are approximately equal number of patients in each stratum. However, simulations based on real trial data found that type I error rates were unaffected by the method of analysis (stratified vs unstratified), indicating these conditions were not met in real datasets. Comparison of different analysis methods found that with small sample sizes and a binary or time-to-event outcome, most analysis methods lead to either inflated type I error rates or a reduction in power; the lone exception was a stratified analysis using random effects for strata, which gave nominal type I error rates and adequate power. Conclusions: It is unlikely that a stratified analysis is necessary after stratified randomisation except in extreme scenarios. Therefore, the method of analysis (accounting for the strata, or adjusting only for the covariates) will not generally need to depend on the method of randomisation used. Most methods of analysis work well with large sample sizes, however treating strata as random effects should be the analysis method of choice with binary or time-to-event outcomes and a small sample size

Reporting on covariate adjustment in randomised controlled trials before and after revision of the 2001 CONSORT statement: a literature review

<p>Abstract</p> <p>Objectives</p> <p>To evaluate the use and reporting of adjusted analysis in randomised controlled trials (RCTs) and compare the quality of reporting before and after the revision of the CONSORT Statement in 2001.</p> <p>Design</p> <p>Comparison of two cross sectional samples of published articles.</p> <p>Data Sources</p> <p>Journal articles indexed on PubMed in December 2000 and December 2006.</p> <p>Study Selection</p> <p>Parallel group RCTs with a full publication carried out in humans and published in English</p> <p>Main outcome measures</p> <p>Proportion of articles reported adjusted analysis; use of adjusted analysis; the reason for adjustment; the method of adjustment and the reporting of adjusted analysis results in the main text and abstract.</p> <p>Results</p> <p>In both cohorts, 25% of studies reported adjusted analysis (84/355 in 2000 vs 113/422 in 2006). Compared with articles reporting only unadjusted analyses, articles that reported adjusted analyses were more likely to specify primary outcomes, involve multiple centers, perform stratified randomization, be published in general medical journals, and recruit larger sample sizes. In both years a minority of articles explained why and how covariates were selected for adjustment (20% to 30%). Almost all articles specified the statistical methods used for adjustment (99% in 2000 vs 100% in 2006) but only 5% and 10%, respectively, reported both adjusted and unadjusted results as recommended in the CONSORT guidelines.</p> <p>Conclusion</p> <p>There was no evidence of change in the reporting of adjusted analysis results five years after the revision of the CONSORT Statement and only a few articles adhered fully to the CONSORT recommendations.</p

Assessment and Implication of Prognostic Imbalance in Randomized Controlled Trials with a Binary Outcome – A Simulation Study

Chance imbalance in baseline prognosis of a randomized controlled trial can lead to over or underestimation of treatment effects, particularly in trials with small sample sizes. Our study aimed to (1) evaluate the probability of imbalance in a binary prognostic factor (PF) between two treatment arms, (2) investigate the impact of prognostic imbalance on the estimation of a treatment effect, and (3) examine the effect of sample size (n) in relation to the first two objectives.We simulated data from parallel-group trials evaluating a binary outcome by varying the risk of the outcome, effect of the treatment, power and prevalence of the PF, and n. Logistic regression models with and without adjustment for the PF were compared in terms of bias, standard error, coverage of confidence interval and statistical power.For a PF with a prevalence of 0.5, the probability of a difference in the frequency of the PF≥5% reaches 0.42 with 125/arm. Ignoring a strong PF (relative risk = 5) leads to underestimating the strength of a moderate treatment effect, and the underestimate is independent of n when n is >50/arm. Adjusting for such PF increases statistical power. If the PF is weak (RR = 2), adjustment makes little difference in statistical inference. Conditional on a 5% imbalance of a powerful PF, adjustment reduces the likelihood of large bias. If an absolute measure of imbalance ≥5% is deemed important, including 1000 patients/arm provides sufficient protection against such an imbalance. Two thousand patients/arm may provide an adequate control against large random deviations in treatment effect estimation in the presence of a powerful PF.The probability of prognostic imbalance in small trials can be substantial. Covariate adjustment improves estimation accuracy and statistical power, and hence should be performed when strong PFs are observed

The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies

Adjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice

Phosphorus dynamics in a tropical forest soil restored after strip mining

Background and aims We hypothesized that successful early ecosystem and soil development in these P-deficient soil materials will initially depend on effective re-establishment of P storage and cycling through organic matter. This hypothesis was tested in a 26-year chronosequence of seven lightly fertilized, oxidic soil materials restored to eucalypt forest communities after bauxite mining. Methods Total P (Pt) status, Hedley P fractions and partial chemical speciation (NaOH-EDTA extraction and analysed using solution 31P NMR spectroscopy) were determined in the restored soils. Results Concentrations of Pt and most Hedley fractions changed with restoration period, declined with depth and were strongly positively correlated with C and N concentrations. Biological P dominated the Labile and Intermediate P fractions while Long-term P was dominantly inorganic. Organic P concentrations in NaOH-EDTA extracts and their chemical natures were similar in restored and unburned native forest sites. Phosphomonoesters were the dominant class of organic P. Conclusions Surprisingly rapid P accretion and fractional changes occurred over 26 years, largely in the surface soils and closely associated with organic matter status. Alkaline hydrolysis products of phosphodiesters and pyrophosphate indicated the importance of microbial P cycling. The important consequences for long-term ecosystem development and biological diversity require further study

Subgroup Analysis via Recursive Partitioning

Subgroup analysis is an integral part of comparative analysis where assessing the treatment effect on a response is of central interest. Its goal is to determine the heterogeneity of the treatment effect across subpopulations. In this paper, we adapt the idea of recursive partitioning and introduce an interaction tree (IT) procedure to conduct subgroup analysis. The IT procedure automatically facilitates a number of objectively defined subgroups, in some of which the treatment effect is found prominent while in others the treatment has a negligible or even negative effect. The standard CART (Breiman et al., 1984) methodology is inherited to construct the tree structure. Also, in order to extract factors that contribute to the heterogeneity of the treatment effect, variable importance measure is made available via random forests of the interaction trees. Both simulated experiments and analysis of census wage data are presented for illustration.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000270824200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Automation &amp; Control SystemsComputer Science, Artificial IntelligenceSCI(E)EI38ARTICLE141-1581