eHealth recruitment challenges

Little is known about effective eHealth recruitment methods. This paper presents recruitment challenges associated with enrolling African-American girls aged 8–10 years in an eHealth obesity prevention program, their effect on the recruitment plan, and potential implications for eHealth research.Although the initial recruitment strategy was literature-informed, it failed to enroll the desired number of girls within a reasonable time period. Therefore, the recruitment strategy was reformulated to incorporate principles of social marketing and traditional marketing techniques. The resulting plan included both targeted, highly specific strategies (e.g., selected churches), and more broad-based approaches (e.g., media exposure, mass mailings, radio advertisements).The revised plan enabled recruitment goals to be attained. Media appeared to be particularly effective at reaching the intended audience. Future research should identify the most effective recruitment strategies for reaching potential eHealth audiences.     

A bivariate normal example where the locally most powerful and distantly most powerful test statistics are beaten everywhere from a median p-value standpoint

In the bivariate normal, n=2 case, when testing H₀:μ_x=μ_y=0,σ² _x=σ² _y=1, ρ=0 vs. H₁:μ_x=μ_y=0,σ² _x=σ² _y=1, 0<ρ<1, it is shown that the median p-values given by the locally most powerful test and the distantly most powerful test are both beaten everywhere by the median of a third test.     

The collected works of john w. tukey

We describe novel, analytical, data-analysis, and Monte-Carlo-simulation studies of strongly heteroscedastic data of both small and wide range.Many different types of heteroscedasticity and fixed or variable weighting are incorporated through error-variance models.Attention is given to parameter bias determinations, evaluations of their significances, and to new ways to correct for bias.The error-variance models allow for both additive and independent power-law errors, and the power exponent is shown to be able to be well determined for typical physicalsciences data by the rapidly-converging, general-purpose, extended-least-squares program we use.The fitting and error-variance models are applied to both low-and high-heteroscedasticity situations, including single-response data from radioactive decay.Monte-Carlo simulations of data with similar parameters are used to evaluate the analytical models developed and the various minimization methods em-ployed, such as extended and generalized least squares.Logarithmic and inversion transformations are investigated in detail, and it is shown analytically and by simulations that exponential data with constant percentage errors can be logarithmically transformed to allow a simple parameter-bias-removal procedure.A more-general bias-reduction approach combining direct and inversion fitting is also developed.Distributions of fitting-model and error-variance-model parameters are shown to be typically non-normal, thus invalidating the usual estimates of parameter bias and precision.Errors in conventional confidence-interval estimates are quantified by comparison with accurate simulation results.     

Disabled or differently-enabled? Dyslexic identities in online forum postings

This study presents a novel exploration into how people with dyslexia construct personal identities within anonymous, unsolicited, postings to an online discussion forum. An interpretational phenomenological analysis suggested three key identities: learning-disabled, differently-enabled and societally-disabled. These are discussed in terms of the extent to which they draw differentially on a discourse of disability as opposed to one of individual difference. The identities are to an extent malleable and overlapping according to context. Postings suggested overall that while some contributors constructed themselves as differently-enabled and celebrated dyslexia-related abilities, others sensed themselves having a disabled identity imposed upon them. Dyslexia is perceived as negatively construed within a society which promotes literacy and cognitive acuity as essential aspects of educational and social competence and where learning differences are poorly tolerated. In addition, this study highlights the potential of online forum contributions in the study of social influences in under-researched groups.     

Citing history

ABSTRACT

Although rarely considered within the existing scholarship on social movements, even a cursory analysis of protest activity suggests that movements regularly invoke historical citations (whether consciously or not) while working to clarify aims and mobilize constituencies. In order to make sense of this process, and to account for the variations that arise among the different citation modalities favored by movements on opposite ends of the political spectrum, I draw upon the theoretical contributions of Marxist cultural critic Walter Benjamin and, in particular, on his exploration of ‘wish images’ and ‘dialectical images,’ their attributes, and their interrelationship. According to Benjamin, such images summoned the past either to project visions of future happiness (as with the wish image) or to deposit the witness before a moment of decisive, present-tense reckoning. After outlining the role of historical citation in social movements and in the broader cultural field through which these movements find expression, I analyze two recent protest events – the ‘Unite the Right’ rally in Charlottesville, VA, in which wish images were actively deployed, and the 2017 Women’s Strike in New York City, where a dialectical image arose from the constellated nodes of the march’s route – to consider the relationship between citation modality and protest outcome. Following from this analysis, and in keeping with the unapologetically partisan nature of my investigation, I conclude by advancing some strategic recommendations for movements seeking – as Benjamin once enjoined – to ‘improve our position in the struggle against Fascism.’     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

Evaluation of Proactive and Reactive Strategies for Polio Eradication Activities in Pakistan and Afghanistan

Only Pakistan and Afghanistan reported any polio cases caused by serotype 1 wild polioviruses (WPV1s) in 2017. With the dwindling cases in both countries and pressure to finish eradication with the least possible resources, a danger exists of inappropriate prioritization of efforts between the two countries and insufficient investment in the two countries to finish the job. We used an existing differential‐equation‐based poliovirus transmission and oral poliovirus (OPV) evolution model to simulate a proactive strategy to stop transmission, and different hypothetical reactive strategies that adapt the quality of supplemental immunization activities (SIAs) in response to observed polio cases in Pakistan and Afghanistan. To account for the delay in perception and adaptation, we related the coverage of the SIAs in high‐risk, undervaccinated subpopulations to the perceived (i.e., smoothed) polio incidence. Continuation of the current frequency and quality of SIAs remains insufficient to eradicate WPV1 in Pakistan and Afghanistan. Proactive strategies that significantly improve and sustain SIA quality lead to WPV1 eradication and the prevention of circulating vaccine‐derived poliovirus (cVDPV) outbreaks. Reactive vaccination efforts that adapt moderately quickly and independently to changes in polio incidence in each country may succeed in WPV1 interruption after several cycles of outbreaks, or may interrupt WPV1 transmission in one country but subsequently import WPV1 from the other country or enable the emergence of cVDPV outbreaks. Reactive vaccination efforts that adapt independently and either more rapidly or more slowly to changes in polio incidence in each country may similarly fail to interrupt WPV1 transmission and result in oscillations of the incidence. Reactive strategies that divert resources to the country of highest priority may lead to alternating large outbreaks. Achieving WPV1 eradication and subsequent successful OPV cessation in Pakistan and Afghanistan requires proactive and sustained efforts to improve vaccination intensity in under‐vaccinated subpopulations while maintaining high population immunity elsewhere.