Narrative Text Ppt Free Download
Karla Pearcy
Students need to learn the purposes and methods of narration in order to understand the narrative framework and to eliminate frustration when they read. When students know the narrative elements, they can more easily follow the story line and make successful predictions about what is to occur. In addition, understanding these elements develops higher-level thinking skills.
Ask students to copy lines from the narrative that relate to the conflict or characters; break the lines into three parts, and write each part on a different color index card. Mix the cards together, and then ask students to work together to find complete lines. Then have them read the line aloud and make a prediction. Example:
Simply put, a narrative text is a type of text that tells a story or describes a sequence of events. The purpose of a narrative text is to entertain or inform the reader by presenting a series of events in a coherent and engaging way.
Some narratives may include additional elements, such as flashbacks, multiple perspectives, or non-linear timelines. Nevertheless, the basic structure described above provides a useful framework for understanding and analyzing narrative texts.
I am required to generate a narrative in paragraph form after asking people a bunch of standardized questions. Right now, I listen to them and type as they are talking. It is horribly time-inefficient, as these are simple questions they could easily answer in check-box form, but the reports have to be in paragraphs.
The authors describe a constructionist theory that accounts for the knowledge-based inferences that are constructed when readers comprehend narrative text. Readers potentially generate a rich variety of inferences when they construct a referential situation model of what the text is about. The proposed constructionist theory specifies that some, but not all, of this information is constructed under most conditions of comprehension. The distinctive assumptions of the constructionist theory embrace a principle of search (or effort) after meaning. According to this principle, readers attempt to construct a meaning representation that addresses the reader's goals, that is coherent at both local and global levels, and that explains why actions, events, and states are mentioned in the text. This study reviews empirical evidence that addresses this theory and contrasts it with alternative theoretical frameworks.
How can I create a section on an analysis/dashboard that is only text? I see in the AWS created admin-console-dashboard a big section on the Landing Page that is only text? How is this configured?
Screenshot from 2022-04-18 13-41-431740899 196 KB
Background: The rate of suicide in the US has increased substantially in the past two decades, and new insights are needed to support prevention efforts. The National Violent Death Reporting System (NVDRS), the nation's most comprehensive registry of suicide mortality, has qualitative text narratives that describe salient circumstances of these deaths. These texts have great potential for providing novel insights about suicide risk but may be subject to information bias.
Objective: To examine the relationship between decedent characteristics and the presence and length of NVDRS text narratives (separately for coroner/medical examiner (C/ME) and law enforcement (LE) reports) among 233,108 suicide and undetermined deaths from 2003-2017.
Methods: Generalized estimating equations (GEE) logistic and quasi-Poisson modeling was used to examine variation in the narratives (proportion of missing texts and character length of the non-missing texts, respectively) as a function of decedent age, sex, race/ethnicity, education, marital status, military history, and homeless status. Models adjusted for site, year, location of death, and autopsy status.
Results: The frequency of missing narratives was higher for LE vs. C/ME texts (19.8% vs. 5.2%). Decedent characteristics were not consistently associated with missing text across the two types of narratives (i.e., Black decedents were more likely to be missing the LE narrative but less likely to be missing the C/ME narrative relative to non-Hispanic whites). Conditional on having a narrative, C/ME were significantly longer than LE (822.44 vs. 780.68 characters). Decedents who were older, male, had less education and some racial/ethnic minority groups had shorter narratives (both C/ME and LE) than younger, female, more educated, and non-Hispanic white decedents.
Conclusion: Decedent characteristics are significantly related to the presence and length of narrative texts for suicide and undetermined deaths in the NVDRS. Findings can inform future research using these data to identify novel determinants of suicide mortality.
I'm thinking about developing a .rmd file that can dynamically write some chunks of narratives in the output file (.html,.pdf,...) based on the preceding R result. To put it simple below is how I want it works:
When knitr processes a document, the document is split into two categories of input: ordinary text and code chunks. Ordinary text stays unchanged and is passed to the output file. Consequently, if plain text is supposed to be included dynamically, it must be inside a chunk.
However, it is cumbersome to cat() lots of text from a R chunk. engine can be used to overcome this. Besides R, there are other engines that can be used to evaluate chunks, among them the (currently undocumented?) engine asis. This engine is very simple. From the knitr NEWS file:
added a new engine named asis to write the chunk content without processing it; it also respects the chunk options echo and eval -- when either one is FALSE, the chunk will be hidden; this makes it possible to write text conditionally
When citing multiple works in a parenthetical in-text citation, place each source in alphabetical order, separated by semicolons, e.g. (Bond University, 2020; Moro & Henson, 2017; Watt et al., 2013).
When citing multiple sources in a narrative citation, they can appear in any order, e.g. Moro and Henson (2017), Watt et al. (2013) and Bond University (2020) all looked at....
Paraphrasing is putting the words and ideas of others into your own words. It is often used a way to summarise an author's ideas and express them more succinctly. You must always use an in-text citation when you paraphrase any source.
You may include page and paragraph numbers in the in-text citation. This is not required, but can be helpful to any readers of your work so they can easily identify where your information has come from.
Place in-text citations at the end of a quote, and include page numbers or paragraphs where possible. Where no page or paragraph is available from a textual work (such as a webpage, or online news article), provide another way of identifying where the quote is located, such as:
For short quotations of fewer than 40 words, incorporate the quote into the text and enclose it with double quotation marks, e.g. "Wellness is not merely the absence of disease or infirmity" (Kent, 2016, p. 107).
Personal communications, such as emails, text messages, personal interviews, unrecorded lectures or speeches and phone calls, are cited in text only. Give the initials and surname of the communicator, and as much of the date as possible, e.g. (D. Crowe, personal communication, January, 2020), E.B. Farnum (personal communication, December 4, 2019).
If referring to a website generally and not a specific page or part of the site, do not include an in-text citation or a reference list entry. Simply include the name of the website in the text with the URL in parentheses, e.g. Useful resources were found using Bond's library research guide Psychology and Counselling ( -counselling).
Methods The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria.
Citation: Mezuk B, Kalesnikava VA, Kim J, Ko TM, Collins C (2021) Not discussed: Inequalities in narrative text data for suicide deaths in the National Violent Death Reporting System. PLoS ONE 16(7): e0254417.
Data Availability: The narrative data used in this analysis are available by request from the CDC through their restricted-access data process. Our use of these restricted-access NVDRS data is governed by a Data Use Agreement (DUA) with the CDC. This DUA legally prohibits us from sharing these data with outside investigators. Any investigator can gain access to these restricted access NVDRS data by contacting nvdr...@cdc.gov and following the procedures outlined here: Other NVDRS data are publicly-available: Cells with
Regardless of the analytic approach used, any effort to draw inferences from the NVDRS narratives need to be made with a careful consideration of potential biases and limitations in data collection and measurement. From a data quality perspective, the NVDRS texts are unique, as they are explicitly written for research purposes by centrally-trained staff. NVDRS staff undergo regular training to enhance consistency of abstraction, and state data are reviewed centrally by CDC staff before they are made available to external investigators [19, 20]. However, these narratives may still be subject to measurement error which could bias inferences [21]. For example, if there are systematic patterns in the amount or quality of text written about each case as a function of decedent characteristics (e.g., age or race), this information bias would impact the validity of any conclusions drawn about how suicide mortality varies over the life course or how established risk factors for suicide (e.g., depression, substance misuse) relate to racial differences in suicide risk, respectively. Investigators need to understand the strengths and limitations of these narrative texts to appropriately account for any such sources of bias in their empirical research.
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