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Young people's perceptions of therapist interpersonal skills and alliance:How distinct are they?
Get PDFThe aim of this study was to investigate the degree of commonality between young people's perceptions of the alliance and the therapist interpersonal skills (TIS) of empathy, unconditional positive regard, and congruence. Participants were 152 young people (aged 13-16 years old; 114 female, 70 of minoritized ethnicities) who were experiencing moderate to severe levels of emotional symptoms and participated in up to 10 sessions of school-based humanistic counseling. At 6 weeks postrandomization, TIS and the alliance were assessed using the Barrett-Lennard Relationship Inventory: Form OS-40: T-S (Student Form, Version 3) and the Working Alliance Inventory Short Form, respectively. Structural equation modeling was used to identify latent constructs. A model with two correlated second-order factors and a hierarchical model with one third-order general factor exhibited the best data fit. The general factor, which we named youth-perceived relationship quality, explained a substantial amount of variance for TIS (81%) and alliance (98%). TIS and alliance effects, treated independently, explained an additional 3% and 1.4% of variance in satisfaction with care and psychological difficulties after therapy, respectively, as compared with general youth-perceived relationship quality. While therapeutic relationship factors-in clinical practice, training, and research-are often considered distinct, we found a high degree of commonality in how young people perceived the alliance and TIS. Further research is warranted on youth-perceived relationship quality as an integrated variable in youth psychotherapy. (PsycInfo Database Record (c) 2025 APA, all rights reserved). </p
Behavioral syndromes are associated with social plasticity and competence in a wild primate
Get PDFThe ability to optimize social behavior to varying socioecological circumstances has been termed “social competence” and relies on behavioral plasticity. Behavioral syndromes, i.e. consistent individual differences in intraindividual correlations among behavioral traits, appear to be a constraint on social competence, yet studies exploring this have largely been limited to experimental laboratory settings. Here, we tested the importance of behavioral syndromes to social competence in wild Barbary macaques (Macaca sylvanus), an endangered primate with established links between positive social relationships and survival. We studied two groups (n = 27 individuals) in which behavioral syndrome phenotypes were established in a previous study. Individuals with lower scores for the “excitable” phenotype (roughly equivalent to the “shy-bold” axis in other studies) showed greater plasticity compared to more “excitable” (i.e., “bolder”) individuals in affiliative responses to the immediate social environment, being more likely to initiate grooming with larger numbers of conspecific bystanders present. Less excitable individuals increased their grooming social network connectivity to a greater degree compared to more excitable individuals in periods of higher anthropogenic pressure. During colder weather, less excitable individuals concentrated their grooming network into fewer ties, whereas more excitable individuals slightly increased their number of connections. Any changes in network connectivity in relation to socioecology were small, reflecting the fact that stability in social network position is a common phenomenon in various taxa. Overall, we show that behavioral syndrome phenotypes influence plasticity in affiliative behavior and thus may be a key factor in individual responses to the rapidly changing socioecologies of the Anthropocene
The 1850s Sustainability Novel: Manufacturers, Serials, and (Eco)systems in Dickens and Gaskell
No full textKnowledge-Grounded Attention-Based Neural Machine Translation Model
No full textNeural machine translation (NMT) model processes sentences in isolation and ignores additional contextual or side information beyond sentences. The input text alone often provides limited knowledge to generate contextually correct and meaningful translation. Relying solely on the input text could yield translations that lack accuracy. Side information related to either source or target side is helpful in the context of NMT. In this study, we empirically show that training an NMT model with target-side additional information used as knowledge can significantly improve the translation quality. The acquired knowledge is leveraged in the encoder-/decoder-based model utilizing multiencoder framework. The additional encoder converts knowledge into dense semantic representation called attention. These attentions from the input sentence and additional knowledge are then combined into a unified attention. The decoder generates the translation by conditioning on both the input text and acquired knowledge. Evaluation of translation from Urdu to English with a low-resource setting yields promising results in terms of both perplexity reduction and improved BLEU scores. The proposed models in the respective group outperform in LSTM and GRU with attention mechanism by +3.1 and +2.9 BLEU score, respectively. Extensive analysis confirms our claim that the translations influenced by additional information may occasionally contain rare low-frequency words and faithful translation. Experimental results on a different language pair DE-EN demonstrate that our suggested method is more efficient and general
Employing Natural Language Processing Techniques for the Development of a Voting-Based POS Tagger in the Urdu Language
No full textThe process of sequence labeling (POS) by assigning syntactic tags to words in the given context is an important role in various NLP applications. The core motive of this work is to tackle the morpho-syntactic category of words in Urdu language. This language has lots of computational challenges because of its dual nature. The work comprises different tasks as initially the authors tracked the best combination of feature sets in terms of CRF to entitle the previous results on two stable and well-known datasets Bushra Jawaid dataset and CLE dataset. Due to syntactic ambiguity, a state-of-the-art voting method has been introduced which is being implemented to overcome the contradictory results of the different machine learning classifiers. The results show significant improvement in the baseline results as the F1-score on a primary dataset is 94.8% and 95.7% on the succeeding dataset. Long short-term memory (LSTM) is used for one of the most diverse and inflectional tasks like part of speech tagging for the Urdu language by achieving an F1-score of 86.7% and 96.1% respectively for both datasets
