A Qualitative Study Examining the Quality of Working Alliance as a Function of the Social Identifies of Clients and Therapists During the Mental Health Intake

Therapists are faced with the challenge of developing effective ways to advance cross-cultural engagement with a rapidly growing diverse client population. In this qualitative study, we characterized the way clients and therapists described the quality of working alliance during the mental health intake and examined whether these descriptions vary as a function of their social identities. We conducted in-depth interviews with Ashkenazi (socially advantaged group; n = 22) therapists and their Mizrahi (socially disadvantaged group n = 29) or Ashkenazi (n = 26) clients immediately following their intake session in four mental health clinics in Israel. We performed a thematic analysis. Overall, interrater reliability among three raters who coded the narratives was high (kappa = 0.72, therapist; 0.70, client). Across all client and therapist interviews, we identified eight central themes detailing different qualities of the working alliance: (1) feeling understood, (2) feeling comfortable, (3) openness and cooperation, (4) trust, (5) empathy and identification, (6) frustration and disappointment, (7) anger and hostility, and (8) emotional disengagement. On average, clients reported 2.56 (standard deviation = 1.17) and therapists described 2.65 (standard deviation = 1.45) themes in each session. Overall, concordant and discordant dyads described similar themes with few exceptions. In particular, being part of a discordant dyad may affect the client’s interpretation of non-verbal communication as well as the therapist’s evaluation of the client’s openness and trustworthiness. Although less frequent, when anger and hostility were described by therapists, these characterized the interaction with Mizrahi clients. We discuss implications to care including the need to promote a culturally humble approach to providing care for minorities

PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging

In-source fragmentation (ISF) is a naturally occurring phenomenon in various ion sources including soft ionization techniques such as matrix-assisted laser desorption/ionization (MALDI). It has traditionally been minimized as it makes the dataset more complex and often leads to mis-annotation of metabolites. Here, we introduce an approach termed PICA (for pixel intensity correlation analysis) that takes advantage of ISF in MALDI imaging to increase confidence in metabolite identification. In PICA, the extraction and association of in-source fragments to their precursor ion results in “pseudo-MS/MS spectra” that can be used for identification. We examined PICA using three different datasets, two of which were published previously and included validated metabolites annotation. We show that highly colocalized ions possessing Pearson correlation coefficient (PCC) ≥ 0.9 for a given precursor ion are mainly its in-source fragments, natural isotopes, adduct ions, or multimers. These ions provide rich information for their precursor ion identification. In addition, our results show that moderately colocalized ions (PCC < 0.9) may be structurally related to the precursor ion, which allows for the identification of unknown metabolites through known ones. Finally, we propose three strategies to reduce the total computation time for PICA in MALDI imaging. To conclude, PICA provides an efficient approach to extract and group ions stemming from the same metabolites in MALDI imaging and thus allows for high-confidence metabolite identification

PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging

In-source fragmentation (ISF) is a naturally occurring phenomenon in various ion sources including soft ionization techniques such as matrix-assisted laser desorption/ionization (MALDI). It has traditionally been minimized as it makes the dataset more complex and often leads to mis-annotation of metabolites. Here, we introduce an approach termed PICA (for pixel intensity correlation analysis) that takes advantage of ISF in MALDI imaging to increase confidence in metabolite identification. In PICA, the extraction and association of in-source fragments to their precursor ion results in “pseudo-MS/MS spectra” that can be used for identification. We examined PICA using three different datasets, two of which were published previously and included validated metabolites annotation. We show that highly colocalized ions possessing Pearson correlation coefficient (PCC) ≥ 0.9 for a given precursor ion are mainly its in-source fragments, natural isotopes, adduct ions, or multimers. These ions provide rich information for their precursor ion identification. In addition, our results show that moderately colocalized ions (PCC < 0.9) may be structurally related to the precursor ion, which allows for the identification of unknown metabolites through known ones. Finally, we propose three strategies to reduce the total computation time for PICA in MALDI imaging. To conclude, PICA provides an efficient approach to extract and group ions stemming from the same metabolites in MALDI imaging and thus allows for high-confidence metabolite identification