Too sick, not sick enough? Effects of treatment type and timing on depression stigma

Stigma as a barrier to early intervention among youth seeking mental health services in Ontario, Canada: a qualitative study

BACKGROUND

Stigma associated with mental health challenges is a major barrier to service seeking among youth. Understanding how stigma impacts service-seeking decisions from the perspectives of youth remains underexplored. Such research is necessary to inform effective stigma reduction.

OBJECTIVE

This study aims to understand how stigma influences service seeking among youth with mental health challenges.

METHODS

Qualitative inquiry was taken using youth engagement, underpinned by pragmatism. Data were collected via 4 virtual focus groups with 22 purposively selected youth participants with lived experience of mental health challenges in Ontario, Canada. Focus group guides were developed collaboratively among research team members, including youth co-researchers. Data were analyzed inductively using reflexive thematic analysis.

RESULTS

Three main themes were constructed from the data: point of entry into the system, being biomedicalized or trivialized, and paving the way for non-stigmatizing services. Initial contact with the mental healthcare system was seen to be affected by stigma, causing participants to delay contact or be refused services if they do not fit with an expected profile. Participants described a constant negotiation between feeling 'sick enough' and 'not sick enough' to receive services. Once participants accessed services, they perceived the biomedicalization or trivialization of their challenges to be driven by stigma. Lastly, participants reflected on changes needed to reduce stigma's effects on seeking and obtaining services.

CONCLUSION

A constant negotiation between being 'sick enough' or 'not sick enough' is a key component of stigma from the perspectives of youth. This tension influences youth decisions about whether to seek services, but also service provider decisions about whether to offer services. Building awareness around the invisibility of mental health challenges and the continuum of wellness to illness may help to break down stigma's impact as a barrier to service seeking. Early intervention models of care that propose services across the spectrum of challenges may prevent the sense of stigma that deters youth from accessing and continuing to access services.

Screening Amazon's Mechanical Turk for Adults With ADHD

Objective: Researchers are increasingly using Amazon's Mechanical Turk (MTurk; www.mturk.com ) to recruit study participants. However, the utility of MTurk for investigations of ADHD in adulthood is unknown. Method: A total of 6,526 MTurk workers (median age range = 26-35 years) completed an online screening survey assessing their diagnostic histories and symptoms of ADHD, as well as relevant demographic correlates and diagnostic comorbidity. Results: The prevalence of MTurk workers who reported being diagnosed as children and as adults, and the percentage of workers whose ADHD appeared to persist from childhood into adulthood, are consistent with those observed in "offline" samples. Relative to MTurk workers diagnosed with ADHD as adults, workers diagnosed with ADHD as children were more likely to be male and without college degrees, as well as less likely to have comorbid depression or anxiety disorders. Conclusion: Amazon's MTurk holds promise as a recruitment tool for studying adults with ADHD.

Mapping of Crowdsourcing in Health: Systematic Review

Background

Crowdsourcing involves obtaining ideas, needed services, or content by soliciting Web-based contributions from a crowd. The 4 types of crowdsourced tasks (problem solving, data processing, surveillance or monitoring, and surveying) can be applied in the 3 categories of health (promotion, research, and care).

Objective

This study aimed to map the different applications of crowdsourcing in health to assess the fields of health that are using crowdsourcing and the crowdsourced tasks used. We also describe the logistics of crowdsourcing and the characteristics of crowd workers.

Methods

MEDLINE, EMBASE, and ClinicalTrials.gov were searched for available reports from inception to March 30, 2016, with no restriction on language or publication status.

Results

We identified 202 relevant studies that used crowdsourcing, including 9 randomized controlled trials, of which only one had posted results at ClinicalTrials.gov. Crowdsourcing was used in health promotion (91/202, 45.0%), research (73/202, 36.1%), and care (38/202, 18.8%). The 4 most frequent areas of application were public health (67/202, 33.2%), psychiatry (32/202, 15.8%), surgery (22/202, 10.9%), and oncology (14/202, 6.9%). Half of the reports (99/202, 49.0%) referred to data processing, 34.6% (70/202) referred to surveying, 10.4% (21/202) referred to surveillance or monitoring, and 5.9% (12/202) referred to problem-solving. Labor market platforms (eg, Amazon Mechanical Turk) were used in most studies (190/202, 94%). The crowd workers’ characteristics were poorly reported, and crowdsourcing logistics were missing from two-thirds of the reports. When reported, the median size of the crowd was 424 (first and third quartiles: 167-802); crowd workers’ median age was 34 years (32-36). Crowd workers were mainly recruited nationally, particularly in the United States. For many studies (58.9%, 119/202), previous experience in crowdsourcing was required, and passing a qualification test or training was seldom needed (11.9% of studies; 24/202). For half of the studies, monetary incentives were mentioned, with mainly less than US $1 to perform the task. The time needed to perform the task was mostly less than 10 min (58.9% of studies; 119/202). Data quality validation was used in 54/202 studies (26.7%), mainly by attention check questions or by replicating the task with several crowd workers.

Conclusions

The use of crowdsourcing, which allows access to a large pool of participants as well as saving time in data collection, lowering costs, and speeding up innovations, is increasing in health promotion, research, and care. However, the description of crowdsourcing logistics and crowd workers’ characteristics is frequently missing in study reports and needs to be precisely reported to better interpret the study findings and replicate them.

Stigma Experienced by Parkinson's Disease Patients: A Descriptive Review of Qualitative Studies

Parkinson's disease (PD) is a neurodegenerative disease characterized by motor and nonmotor symptoms. Both of them imply a negative impact on Health-Related Quality of Life. A significant one is the stigma experienced by the parkinsonian patients and their caregivers. Moreover, stigma may affect everyday life and patient's subjective and relational perception and it may lead to frustration and isolation. Aim of the present work is to qualitatively describe the stigma of PD patients stemming from literature review, in order to catch the subjective experience and the meaning of the stigma construct. Literature review was performed on PubMed database and Google Scholar (keywords: Parkinson Disease, qualitative, stigma, social problem, isolation, discrimination) and was restricted to qualitative data: 14 articles were identified to be suitable to the aim of the present overview. Results are divided into four core constructs: stigma arising from symptoms, stigma linked to relational and communication problems, social stigma arising from sharing perceptions, and caregiver's stigma. The principal relations to these constructs are deeply analyzed and described subjectively through patients' and caregiver's point of view. The qualitative research may allow a better understanding of a subjective symptom such as stigma in parkinsonian patients from an intercultural and a social point of view.

Rapid grading of fundus photographs for diabetic retinopathy using crowdsourcing

Background

Screening for diabetic retinopathy is both effective and cost-effective, but rates of screening compliance remain suboptimal. As screening improves, new methods to deal with screening data may help reduce the human resource needs. Crowdsourcing has been used in many contexts to harness distributed human intelligence for the completion of small tasks including image categorization.

Objective

Our goal was to develop and validate a novel method for fundus photograph grading.

Methods

An interface for fundus photo classification was developed for the Amazon Mechanical Turk crowdsourcing platform. We posted 19 expert-graded images for grading by Turkers, with 10 repetitions per photo for an initial proof-of-concept (Phase I). Turkers were paid US $0.10 per image. In Phase II, one prototypical image from each of the four grading categories received 500 unique Turker interpretations. Fifty draws of 1-50 Turkers were then used to estimate the variance in accuracy derived from randomly drawn samples of increasing crowd size to determine the minimum number of Turkers needed to produce valid results. In Phase III, the interface was modified to attempt to improve Turker grading.

Results

Across 230 grading instances in the normal versus abnormal arm of Phase I, 187 images (81.3%) were correctly classified by Turkers. Average time to grade each image was 25 seconds, including time to review training images. With the addition of grading categories, time to grade each image increased and percentage of images graded correctly decreased. In Phase II, area under the curve (AUC) of the receiver-operator characteristic (ROC) indicated that sensitivity and specificity were maximized after 7 graders for ratings of normal versus abnormal (AUC=0.98) but was significantly reduced (AUC=0.63) when Turkers were asked to specify the level of severity. With improvements to the interface in Phase III, correctly classified images by the mean Turker grade in four-category grading increased to a maximum of 52.6% (10/19 images) from 26.3% (5/19 images). Throughout all trials, 100% sensitivity for normal versus abnormal was maintained.

Conclusions

With minimal training, the Amazon Mechanical Turk workforce can rapidly and correctly categorize fundus photos of diabetic patients as normal or abnormal, though further refinement of the methodology is needed to improve Turker ratings of the degree of retinopathy. Images were interpreted for a total cost of US $1.10 per eye. Crowdsourcing may offer a novel and inexpensive means to reduce the skilled grader burden and increase screening for diabetic retinopathy.