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Los estudios sobre felicidad incluyen diversas perspectivas, conceptualizaciones y factores asociados a este concepto; sin embargo, las investigaciones sobre felicidad con frecuencia se concentran en la dimensión individual de la misma. El presente trabajo hace énfasis en la concepción de la felicidad como un constructo multidimensional conformado por aspectos individuales y por aspectos colectivos. De manera que el propósito central del artículo es presentar un análisis sobre la dimensión colectiva de la felicidad, la cual ha sido poco estudiada en el contexto latinoamericano. En particular este artículo busca enfatizar el papel de las relaciones afectivas, la conducta prosocial y la inversión en los demás, en la comprensión de la felicidad. En las conclusiones se identifican temáticas relevantes asociadas a la dimensión colectiva que permitirán ampliar la investigación sobre la felicidad.

Internationally, there is a gap in high‐school completion rates for Indigenous and non‐Indigenous students. In Australia, gap estimates are commonly based on lag indicators, precluding examination of underlying mechanisms. Using two longitudinal representative samples of Australian youth, we explored differences in high‐school completion between Australian Indigenous and non‐Indigenous rates, and whether the gap varies for students of similar academic ability. Using an intersectional approach, we show the Indigenous gap is significant, is mostly a function of differences in academic achievement, but varies by socioeconomic status (SES) and location. Specifically, high SES and living in urban settings are protective factors for non‐Indigenous students, but not for Indigenous students. Conversely, rural and poor non‐Indigenous students appeared to have dropout rates as large or even larger than similarly poor and rural Indigenous youth. Overall, the results suggest the need for a more nuanced perspective on ‘Indigenous gaps’ in educational attainment.

Many psychological treatments have been shown to be cost-effective and efficacious, as long as they are implemented faithfully. Assessing fidelity and providing feedback is expensive and time-consuming. Machine learning has been used to assess treatment fidelity, but the reliability and generalisability is unclear. We collated and critiqued all implementations of machine learning to assess the verbal behaviour of all helping professionals, with particular emphasis on treatment fidelity for therapists. We conducted searches using nine electronic databases for automated approaches of coding verbal behaviour in therapy and similar contexts. We completed screening, extraction, and quality assessment in duplicate. Fifty-two studies met our inclusion criteria (65.3% in psychotherapy). Automated coding methods performed better than chance, and some methods showed near human-level performance; performance tended to be better with larger data sets, a smaller number of codes, conceptually simple codes, and when predicting session-level ratings than utterance-level ones. Few studies adhered to best-practice machine learning guidelines. Machine learning demonstrated promising results, particularly where there are large, annotated datasets and a modest number of concrete features to code. These methods are novel, cost-effective, scalable ways of assessing fidelity and providing therapists with individualised, prompt, and objective feedback.

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