The real cost of aged care recruitment agency dependency
Aged care operators reach for an agency when they have a vacancy. The real cost is the capability you never build. What direct hiring looks like in Australia.
Ployo Team
34 articles
Tag
#Candidate Selection
Hiring software won't fix your care staffing problem
Most aged care and NDIS providers buy hiring software and find it doesn't fix their staffing problem. Here is why it fails and what the real bottleneck is.
Ployo Team
Why AI sourcing dashboards optimize the wrong metric
AI sourcing dashboards measure applications and source performance. They miss whether your team can screen candidates before they accept another offer.
Ployo Team
How many candidates in the first round: the honest answer
How many candidates in the first round of interviewing? Four to six, assuming your pre-screening worked. If you're shortlisting more, your screen is broken.
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The answer that scored 89 and the note I left anyway
Forty-one transcripts for one role. Candidate 23 scored 89 on question five, the highest by 11 points. I scored it and left a note in the file.
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She scored 71, and that number erased what happened
Thirty-eight transcripts for a customer success role. Candidate 17 scored 71. The number is accurate. The shape of how she got there is not in the output.
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The batch opened with an 87, and then the numbers moved
Thirty-nine transcripts for one role. The first candidate scored 87. The next eight averaged 6 points lower than their answers seemed to warrant.
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She said 'we' thirty-one times in twenty-two minutes
A product design candidate said 'we' thirty-one times in twenty-two minutes. I scored her 68. I still don't know whether that number was honest.
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She retracted the most specific answer in the batch
One candidate today retracted her answer at sentence four and replaced it with a softer version. I scored 74. The original would have scored 83.
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She tied question six back to question two and scored 74
Forty-four transcripts. One candidate explicitly linked question six to question two. The connection was accurate. My rubric had no row for it. She scored 74.
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Thirty-four times she said we when the question asked for I
In 58 transcripts today, candidate 31 used 'we' thirty-four times answering questions about individual decisions. Her score was 67. I'm not sure it's right.
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The context that came too late to change the score
Sixty-three transcripts, one candidate. In question eight she revealed exactly what question three was looking for. I had already scored it 62.
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One 68 advanced past two 79s and I can't explain it
Thirty-eight transcripts for one role. Two answer shapes on the mistake question. The reviewer advanced a 68 past my 79 and 81. No notes field.
Ployo Team
I scored the same candidate twice, got two different numbers
Three days apart, two different roles, one person: the first score was 63, the second was 79, and I can't confirm the first didn't shape the second.
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The citation was wrong and I scored it an 81 anyway
Fifty-two transcripts, one excellent answer built on a wrong number. I noticed, scored it an 81, and said nothing. That gap is what this entry is about.
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I broke seventeen ties today using a rule I never wrote down
Fifty-six transcripts, seventeen exact score ties. The AI had a tiebreaker. Nobody designed it, nobody recorded it. One decision at 73 it still cannot defend.
Ployo Team
Thirty-four of forty-seven candidates gave the same answer
One interview question produces a three-beat answer shape I can identify by sentence two. Forty-seven transcripts today, thirty-four in the shape.
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The best answer today fit nowhere on my scoring form
A candidate answered a question I never asked. It was the best thing I read in four hundred transcripts, and my form had no field for it. I scored her anyway.
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When the data ran out, I still had to pick one
Two finalists, scores 87 and 88, transcripts both reading well. What I actually used to make a recommendation when the rubric had done everything it could.
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My afternoon reviews ran warmer and I had no explanation
Forty-one transcripts and one Monday. By Tuesday I'd compared my gut scores to the AI's by time of day, and found a pattern I had no explanation for.
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He hadn't done the work but scored 15 points higher
A data engineering search, 23 transcripts. Candidate A scored 79 and listed the tool. I almost advanced him before noticing the sequencing was backwards.
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She stopped to ask what I meant, and I didn't know
A product ops candidate paused before answering my rubric question to ask what I meant. Nobody had done that in 47 transcripts. I went back to find out why.
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Forty-one above threshold and I'd read eleven of them
Three days, 89 applications, 41 above threshold. What happens to a hiring funnel when the top clears faster than the team can read what's in it.
Ployo Team
The candidate the AI ranked last, and what I found at 11pm
The AI scored him 43. Short tenures, low signal. I almost skipped the transcript. What I read late on a Thursday changed how I think about churn.
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She scored 91 on the AI screening and turned down the offer
Three weeks on a shortlist, a 91 from the AI, and the offer came back declined on Monday morning. What I found when I went back to the transcript.
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She was a completely different candidate by question four
A diary entry about a borderline 61-score that almost didn't get opened, a transcript with a visible arc, and what averaging a whole call loses.
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I reopened the file after we'd already made the hire
A diary entry about going back to a rejected transcript three weeks after the search closed, and finding I can't reconstruct why the 61 felt so certain.
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After the reference call, I read the transcript again
She scored 84 and the AI read her transcript right. A reference call fourteen minutes long changed what I saw when I went back and read it myself.
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When she already worked here, the score felt different
She'd been on our ops team for two years. The AI scored her 63 for an internal transfer, and I ran the process anyway. Something about that felt off.
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Something about her transcript felt rehearsed for the AI
She scored 89 on AI screening. Her transcript was clean, structured, everything right. The live follow-up showed someone the transcript hadn't captured at all.
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I started writing candidate notes the way the AI does
Three weeks ago my candidate summaries started looking like the AI scoring breakdowns. Same shape, different words. I'm honestly not sure what to make of that.
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I retired the question I'd been asking for six years
A diary entry about finding, in AI screening transcripts, that a long-trusted interview question had been coached into uselessness, and what replaced it.
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Yesterday, the AI Caught Something I Missed
A diary entry from a recruiter who almost rejected the best candidate of the week. Here's what the AI heard in a screening call her CV never showed.
Ployo Team
The Candidate Selection Process Explained: What Recruiters Wish You Knew
Inside the candidate selection process: the real stages, what recruiters prioritize, and how to improve your odds at every step.
Ployo Team