"Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling" - Information and Links:

Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling - Info and Reading Options


“Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling” Metadata:

  • Title: ➤  Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling
  • Authors:

Edition Identifiers:

  • Internet Archive ID: osf-registrations-584fc-v1

AI-generated Review of “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling”:


"Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling" Description:

The Internet Archive:

Client dropout, also known as premature or unilateral termination, is a prevalent problem in psychotherapy, and it has problematic consequences for patients and mental health clinics. Dropout has been defined in a variety of ways, including therapist judgment, failure to complete a specified number of sessions, and failure to achieve reliable change. However, most research does not conceptualize it as missing a scheduled appointment, despite this being the form of dropout that is most costly to mental health providers and prevents other patients from being seen (Barrett et al., 2008). This is especially important at college counseling centers, which have seen an increase in the demand for services (Lipson et al., 2019) and which often have significant wait lists (LeViness et al., 2018). There is a general consensus that more therapy is better, and research on the factors associated with dropout has led to the development of interventions designed to reduce its occurrence and increase the number of therapy sessions (Oldham et al., 2012). However, there is evidence to suggest that some of those who drop out show improvement and may have gotten what they needed from therapy (April & Nicholas, 1996; Buizza et al., 2019; O’Keefe et al., 2019). As such, attempts to prolong therapy may actually be counterproductive for those individuals. The data in support of some dropouts getting what they need is largely qualitative, as quantitative studies typically take an average-level approach and may fail to represent those individuals (Bartholomew et al., 2019; Saxon et al., 2017). This study aims to address this gap in the literature by using latent growth mixture modeling (LGMM) and latent class growth analysis (LCGA), techniques that identify subgroups of individuals who share similar trajectories (Frankfurt et al., 2016). This research has the potential to further our understanding of those who drop out of therapy and has implications for which dropout reduction strategies are most appropriate.

Read “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling”:

Read “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling” by choosing from the options below.

Available Downloads for “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling”:

"Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling" is available for download from The Internet Archive in "data" format, the size of the file-s is: 0.10 Mbs, and the file-s went public at Fri Sep 10 2021.

Legal and Safety Notes

Copyright Disclaimer and Liability Limitation:

A. Automated Content Display
The creation of this page is fully automated. All data, including text, images, and links, is displayed exactly as received from its original source, without any modification, alteration, or verification. We do not claim ownership of, nor assume any responsibility for, the accuracy or legality of this content.

B. Liability Disclaimer for External Content
The files provided below are solely the responsibility of their respective originators. We disclaim any and all liability, whether direct or indirect, for the content, accuracy, legality, or any other aspect of these files. By using this website, you acknowledge that we have no control over, nor endorse, the content hosted by external sources.

C. Inquiries and Disputes
For any inquiries, concerns, or issues related to the content displayed, including potential copyright claims, please contact the original source or provider of the files directly. We are not responsible for resolving any content-related disputes or claims of intellectual property infringement.

D. No Copyright Ownership
We do not claim ownership of any intellectual property contained in the files or data displayed on this website. All copyrights, trademarks, and other intellectual property rights remain the sole property of their respective owners. If you believe that content displayed on this website infringes upon your intellectual property rights, please contact the original content provider directly.

E. Fair Use Notice
Some content displayed on this website may fall under the "fair use" provisions of copyright law for purposes such as commentary, criticism, news reporting, research, or educational purposes. If you believe any content violates fair use guidelines, please reach out directly to the original source of the content for resolution.

Virus Scanning for Your Peace of Mind:

The files provided below have already been scanned for viruses by their original source. However, if you’d like to double-check before downloading, you can easily scan them yourself using the following steps:

How to scan a direct download link for viruses:

  • 1- Copy the direct link to the file you want to download (don’t open it yet).
    • (a free online tool) and paste the direct link into the provided field to start the scan.
  • 2- Visit VirusTotal (a free online tool) and paste the direct link into the provided field to start the scan.
  • 3- VirusTotal will scan the file using multiple antivirus vendors to detect any potential threats.
  • 4- Once the scan confirms the file is safe, you can proceed to download it with confidence and enjoy your content.

Available Downloads

  • Source: Internet Archive
  • All Files are Available: Yes
  • Number of Files: 5
  • Number of Available Files: 5
  • Added Date: 2021-09-10 18:22:06
  • Scanner: Internet Archive Python library 1.9.9

Available Files:

1- ZIP

  • File origin: original
  • File Format: ZIP
  • File Size: 0.00 Mbs
  • File Name: bag.zip
  • Direct Link: Click here

2- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: osf-registrations-584fc-v1_files.xml
  • Direct Link: Click here

3- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: osf-registrations-584fc-v1_meta.sqlite
  • Direct Link: Click here

4- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: osf-registrations-584fc-v1_meta.xml
  • Direct Link: Click here

5- Archive BitTorrent

  • File origin: metadata
  • File Format: Archive BitTorrent
  • File Size: 0.00 Mbs
  • File Name: osf-registrations-584fc-v1_archive.torrent
  • Direct Link: Click here

Search for “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling” downloads:

Visit our Downloads Search page to see if downloads are available.

Find “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling” in Libraries Near You:

Read or borrow “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling” from your local library.

Buy “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling” online:

Shop for “Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling” on popular online marketplaces.



Find "Investigating Psychotherapy No-Shows Using Latent Growth Mixture Modeling" in Wikipdedia