Update README.md

README.md

@@ -6,10 +6,16 @@ widget:
   example_title: 'Ex2: SES'
 - text: ' There is growing evidence of deficits in defensive reactivity (indexed by the startle blink reflex) in individuals diagnosed with antisocial personality disorder (ASPD). However, to date, no study has examined the role of defensive reactivity in the quality of life (QoL) of individuals with ASPD. In the current study, we therefore explored whether the startle blink reflex is negatively associated with QoL in 143 individuals diagnosed with ASPD. Defensive reactivity was measured using a fear-potentiated startle reflex test. To assess QoL, participants completed the Short Form (36) Health Survey (SF-36). Startle blink reflex potentiation deficits during aversive picture viewing were common in the sample (62.3%). Blink reflex potentiation was negatively and significantly associated with QoL. In sum, these findings provide clear evidence that deficits in defensive reactivity are linked to poor QoL in ASPD.'
   example_title: 'Ex3: Reactivity'
-- text: 'While the experimental manipulation was successful, there was no effect on SMR-BCI performance.'
+- text: >-
+    While the experimental manipulation was successful, there was no effect on
+    SMR-BCI performance.
   example_title: 'Ex4: Manipulation Check 1: Successful manipulation check + negative result '
-- text: 'While the experimental manipulation was unsuccessful, there was an effect on SMR-BCI performance.'
-  example_title: 'Ex5: Manipulation Check 2: Unsuccessful manipulation check + positive result '
+- text: >-
+    While the experimental manipulation was unsuccessful, there was an effect on
+    SMR-BCI performance.
+  example_title: >-
+    Ex5: Manipulation Check 2: Unsuccessful manipulation check + positive
+    result 
 pipeline_tag: text-classification
 tags:
 - metascience
@@ -19,13 +25,13 @@ tags:
 ---
 
 ## Model
-SciBERT text classification model for positive and negative results prediction in scientific abstracts of clinical psychology and psychotherapy. 
+SciBERT text classification model for positive and negative results prediction in scientific abstracts of clinical psychology and psychotherapy. The preprint for this project is available on [PsyArxiv](https://osf.io/preprints/psyarxiv/uxyzh).
 
 ## Data
 We annotated over 1,900 clinical psychology abstracts into two categories: 'positive results only' and 'mixed or negative results', and trained models using SciBERT.
 The SciBERT model was validated against one in-domain (clinical psychology) and two out-of-domain data sets (psychotherapy). We compared model performance with Random Forest and three further benchmarks: natural language indicators of result types, *p*-values, and abstract length.
 SciBERT outperformed all benchmarks and random forest in in-domain (accuracy: 0.86) and out-of-domain data (accuracy: 0.85-0.88). 
-Further information on documentation, code and data for the project "Publication Bias Research in Clincial Psychology Using Natural Language Processing" can be found on this [GitHub repository](https://github.com/PsyCapsLock/PubBiasDetect).
+Further information on documentation, code and data for the preprint "Classifying Positive Results in Clinical Psychology Using Natural Language Processing" can be found on this [GitHub repository](https://github.com/schiekiera/PubBiasDetect).
 
 ## Using the model on Huggingface
 The model can be used on Hugginface utilizing the "Hosted inference API" in the window on the right.
@@ -66,4 +72,7 @@ from our [GitHub repository](https://github.com/PsyCapsLock/PubBiasDetect).
 
 ## Disclaimer
 This tool is developed to analyze and predict the prevalence of positive and negative results in scientific abstracts based on the SciBERT model. While publication bias is a plausible explanation for certain patterns of results observed in scientific literature, the analyses conducted by this tool do not conclusively establish the presence of publication bias or any other underlying factors. It's essential to understand that this tool evaluates data but does not delve into the underlying reasons for the observed trends.
-The validation of this tool has been conducted on primary studies from the field of clinical psychology and psychotherapy. While it might yield insights when applied to abstracts of other fields or other types of studies (such as meta-analyses), its applicability and accuracy in such contexts have not been thoroughly tested yet. The developers of this tool are not responsible for any misinterpretation or misuse of the tool's results, and encourage users to have a comprehensive understanding of the limitations inherent in statistical analysis and prediction models
+The validation of this tool has been conducted on primary studies from the field of clinical psychology and psychotherapy. While it might yield insights when applied to abstracts of other fields or other types of studies (such as meta-analyses), its applicability and accuracy in such contexts have not been thoroughly tested yet. The developers of this tool are not responsible for any misinterpretation or misuse of the tool's results, and encourage users to have a comprehensive understanding of the limitations inherent in statistical analysis and prediction models.
+
+## Funding
+This project was funded by the Berlin University Alliance.