Maintaining the total dissolved solids (TDS) content of desalinated seawater within regulated levels is critical for meeting regulatory limits and maintaining public confidence in the quality of the water supply. A number of strategies for maintaining TDS above the target value of 100 ppm while maintaining all other water quality parameters are described as employed in case studies in seawater desalination plants in the Kingdom of Saudi Arabia: These are reduction of second-stage reverse osmosis, addition of seawater, and increasing the extent of remineralisation. The main operational challenge found in increasing TDS is avoiding corrosion by maintaining the Larson–Skold index below acceptable levels. While acceptable results above 100 ppm can be achieved with relative ease, values above 150 ppm are invariably accompanied by Larson–Skold index levels above 1.2, which cannot easily be remedied without significant capital investment. As legacy thermal desalination plants are retired, it will no longer be necessary to take specific action to maintain TDS above minimum levels, and maintaining system stability at higher levels will become the more significant challenge. Finding means to address the high Larson–Skold index levels is thus a priority in the medium-term to avoid potential corrosion of the existing transmission systems, which were designed for conveyance of product water from thermal desalination plants.

Introduction. The Macleod Clark Professional Identity Scale (MCPIS-9) is a tool to assess Professional identity (PI) in pharmacy students.¹ Despite validation, a recent review found it had undergone limited psychometric testing.²

Aims. To examine the construct validity of the MCPIS-9 using Rasch analysis.

Methods. Undergraduate pharmacy students across all year levels completed an adjusted MCPIS-9 with a seven point strongly disagree/ strongly agree response scale and specific referral to the pharmacy profession. The fit of the MCPIS-9 to the polytomous Rasch model was evaluated using RUMM2030.

Results. The MCPIS-9 was completed by 109 pharmacy students. PI scores were positively skewed, ranging from 27 to the maximum of 54 (mean: 43±6.8). Initial assessment of the MCPIS-9 fit to the Rasch model identified severe miss-targeting and disorder thresholds; these were not improved by collapsing response categories. Consequently the mid-point was removed and response categories collapsed into 4. There was evidence of response dependence for the negatively worded questions. The two questions relating specifically to students were most redundant and removed. Unidimensionality of the 7-item scale was explored using principle components analysis of the residuals. Investigation of the 2 highly positive items and 3 highly negative items using a t-test showed the number of responses that differed by more than 5% was 6.5% (95CI 2.5% to 11.4%) of responses indicating unidimensionality. The resulting scale showed good fit to the Rasch model with a non-significant item-trait interaction total χ²=10.3 (df=14, p=0.74) and PSI=0.74. Targeting is reasonable except for one threshold on an item that had no disagree responses.

Discussion. The high Cronbach’s α reported for the MCPIS-9² is artificially inflated by the response dependence between 3 of the items. There is a large positive skew to responses that can be controlled by removing the mid-point and collapsing categories. In future, consideration should be given to using a 7-item tool with an even number of response categories that may also be tested in health professionals as well as students.

Spatial–temporal metrics offer a promising framework for quantifying collective tactical behavior in rugby union, yet their application across multiple matches and try-scoring contexts remains limited. Building on a single-match case study introducing these metrics to rugby union, this study examined how collective spatial–temporal organization relates to try-scoring in elite professional rugby union. GPS-derived positional data from 118 players across six Super Rugby matches were analyzed for the 30 s preceding 43 try-scoring events. Centroid distance, convex hull, stretch index, and approximate entropy (ApEn) were calculated for both attacking and defending teams. Linear mixed-effects models assessed the influence of try source, field location, and match quarter on spatial–temporal organization. Linebreaks (23%) were the most frequent try-scoring precursor. No significant between-phase differences were observed in stretch index or convex hull, in contrast to our earlier single-match case study, suggesting that this finding may not generalize across matches and warrants further multi-match verification. Tries from linebreaks, mauls, and offloads occurred at significantly greater inter-team centroid distances compared to tackle breaks (e.g., linebreaks: 3.6 m greater than tackle breaks, p = 0.023), while offloads and wide passes were associated with higher centroid distance ApEn, indicating greater spatial unpredictability. It appears that inter-team spacing and its unpredictability, rather than unilateral spatial expansion, are key spatial–temporal features of successful attacking play. Centroid distance metrics offer coaches and analysts a practical means of benchmarking attacking spacing by try source and field zone, while ApEn provides a complementary indicator of movement predictability that could inform defensive alignment strategies against attacking sets associated with greater tactical unpredictability. As only successful try-scoring sequences were analyzed, and several try source comparisons (e.g., mauls, grubber kicks) were based on very few observations, these findings should be interpreted as preliminary characteristics associated with successful tries rather than as factors discriminating scoring success from unsuccessful attacking outcomes. Multi-season replication and inclusion of unsuccessful attacking sequences are needed to confirm their generalizability and practical application.