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This week's research paper will discuss four challenges and solutions for incorporating evidence. Please identify challenges and solutions in your paper. Use a heading or subheading to identify each challenge and solution (combined), so you have at least two pages of written content. This week's reference limit is one outside source per challenge/solution (combined). Please make sure your paper is at least two pages long, plus a separate reference page (minimum). This seventh component requires four A-P-A-formatted outside sources. Save your assignment with your name and activity number (or description). Don't use special characters or punctuation.

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Incorporating Gathered Evidence

Many different federal and state data regulations are followed during the data gathering process. A uniform application of these regulations is unlikely, and some of them may even be mutually exclusive. As a result, this may affect how easily data can be compared. When an individual's or the public's immediate safety is at stake, data collection may be limited. There may be little incentive to enhance official data collection if organizations do not have the resources to fulfill the needs of certain categories of individuals. Businesses that do not cater specifically to people of a certain sexual orientation, racial heritage, or with a disability may not see it as necessary to know whether their customers identify as gay, black, Hispanic, Asian, or disabled.

Inconsistent data collection standards

The collection of standard data items and demographic information can be aided by following the guidelines provided by data standards. Examples of standardization aspects that govern regularly implemented data collection procedures include data definitions, standardized questions, and permissible response possibilities. In today's world, administrative data collection adheres to a great number of federal and state data requirements. These rules are not always enforced in the same manner, and they may even be inconsistent with one another, both of which have the potential to impact the comparability of the data that is gathered. For instance, when it comes to the compilation of statistics on people with disabilities, the National Disability Insurance Scheme (NDIS), the National Disability Agreement, and state governing organizations each employ their own unique set of criteria and guidelines. Because each service must determine what is most essential to the customers they serve, the standards for each category of service may be different from one another. For instance, medical services may be more motivated to collect information about a patient through diagnoses and medical histories, whereas non-disability specific services may be more interested in obtaining information regarding support requirements or the need for suitable accommodations. This difference in motivation may be since medical services are more likely to have access to a patient's medical records. If the amount of detail and scope of information gathered by multiple services is inconsistent, it may be challenging to compare the results of different services or to the results of population-level data sets (Duan et al., 2020).

Context of data collection

It is challenging to obtain an accurate information when data collection from customers takes place in a range of contexts and situations. This may have an effect on the amount of information that is acquired from customers. The majority of people who collect data also have another job that is more focused on providing a service (for example, as a police officer, a member of the support staff, or a medical professional). Although data collection is frequently an important part of their jobs, it is not always their primary responsibility. Data collection may be restricted in the event of an emergency or crisis, when a person's safety is the main priority, or when an individual's privacy could be violated by queries regarding family violence (for example, in a busy waiting area). If organizations do not have the resources to meet the requirements of specific populations of people, there may be little motivation to improve the data that is officially collected on those people. It's possible that certain programs, like those dealing with family violence, don't collect comprehensive statistics on this group because they don't have enough money to handle the needs of children. On the other hand, a more extensive collection of data on priority areas can provide a good evidence base on which to analyze the efficacy of alternative funding strategies. This evidence base can help determine whether alternative funding strategies are effective (Reveley & Reveley, 1981).

Insufficient Data Collection of Business Function

Both the quantity and quality of the data that an organization collects may be impacted by its core operations and the time constraints associated with service delivery. Administrative data obtained because of operational demands or to fulfil an internal business obligation may contain some of the most fundamental information, such as a client's contact details. Knowledge of a person's sexual orientation, ethnic background, or handicap may not be regarded as vital to the running of enterprises that do not specialize in serving people with such characteristics, as these types of customers are not their primary customers. This means that organizations are only able to collect a small subset of the information they want in order to achieve large secondary use goals such as conducting research, monitoring, or analysis on a state-wide scale ("Editorial Board," 2020).

Complexity

People of color, LGBTI individuals, and people with disabilities may require more than one piece of knowledge to fully comprehend their past. People who face the greatest dangers and obstacles in gaining access are typically disregarded when this occurs. It is also feasible that this might contribute to the miscommunication that already occurs over issues that people from diverse backgrounds find difficult to fully comprehend. For instance, neglecting to adequately recognize and represent CALD communities by identifying them by the need for an interpreter or putting people and groups under the umbrella term "LGBTI" poses a threat to the integrity of the data. Another example would be combining individuals and organizations under the umbrella word "LGBTQ."

Conclusion

Data collection complies with numerous federal and state requirements. These laws may not even be applied consistently and in the same manner as one another. It can be challenging to obtain comprehensive and trustworthy information when consumer data is gathered in a range of settings and scenarios. When someone's safety is the first priority or there is an emergency, data collection may be restricted. There may not be much incentive for organizations to improve the data that is formally collected if they lack the means to address the needs of populations of people. A more thorough collection of data on priority areas could provide an improved evidence basis from which to evaluate the effectiveness of different funding options. Understanding a person's sexual orientation, ethnicity, or disability may not be viewed as essential to the operation of businesses that do not specialize in catering to those with such traits. It's also feasible that it could add to the confusion surrounding ideas that people with different backgrounds find challenging to fully comprehend.

Ref

Duan, S., Duan, Z., Li, R., Wilson, A., Wang, Y., Jia, Q., Yang, Y., Xia, M., Wang, G., Jin, T., Wang, S., & Chen, R. (2020). Corrigendum to "Bullying victimization, bullying witnessing, bullying perpetration and suicide risk among adolescents: A serial mediation analysis". [Journal of Affective Disorders Volume 273, 1 August 2020, Pages 274-279]. Journal of Affective Disorders. https://doi.org/10.1016/j.jad.2020.11.009

Reveley, A. M., & Reveley, M. A. (1981). The distinction of primary and secondary affective disorders. Journal of Affective Disorders, 3(3), 273-279. https://doi.org/10.1016/0165-0327(81)90028-8

Editorial Board. (2020). Journal of Affective Disorders, 273, ii. https://doi.org/10.1016/s0165-0327(20)32334-x